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Background: WHO, as requested by its member states, launched the Expanded Programme on Immunization (EPI) in 1974 to make life-saving vaccines available to all globally. To mark the 50-year anniversary of EPI, we sought to quantify the public health impact of vaccination globally since the programme's inception., Methods: In this modelling study, we used a suite of mathematical and statistical models to estimate the global and regional public health impact of 50 years of vaccination against 14 pathogens in EPI. For the modelled pathogens, we considered coverage of all routine and supplementary vaccines delivered since 1974 and estimated the mortality and morbidity averted for each age cohort relative to a hypothetical scenario of no historical vaccination. We then used these modelled outcomes to estimate the contribution of vaccination to globally declining infant and child mortality rates over this period., Findings: Since 1974, vaccination has averted 154 million deaths, including 146 million among children younger than 5 years of whom 101 million were infants younger than 1 year. For every death averted, 66 years of full health were gained on average, translating to 10·2 billion years of full health gained. We estimate that vaccination has accounted for 40% of the observed decline in global infant mortality, 52% in the African region. In 2024, a child younger than 10 years is 40% more likely to survive to their next birthday relative to a hypothetical scenario of no historical vaccination. Increased survival probability is observed even well into late adulthood., Interpretation: Since 1974 substantial gains in childhood survival have occurred in every global region. We estimate that EPI has provided the single greatest contribution to improved infant survival over the past 50 years. In the context of strengthening primary health care, our results show that equitable universal access to immunisation remains crucial to sustain health gains and continue to save future lives from preventable infectious mortality., Funding: WHO., Competing Interests: Declaration of interests CLT and KAMG assert that their employer, Imperial College, receives funding for the Vaccine Impact Modelling Consortium from the Bill & Melinda Gates Foundation; Gavi, the Vaccine Alliance; and the Wellcome Trust. CLT has received consulting fees from GSK for attending an advisory board meeting on CMV vaccines in May, 2022 and is pro bono Chair of the Scientific Advisory Panel of the Meningitis Research Foundation. HF asserts that her employer, London School of Hygiene & Tropical Medicine, receives funding for the Vaccine Impact Modelling Consortium from the Gates Foundation. JFM asserts that his employer, University of Washington, receives grant funding from Gavi and from the Gates Foundation. KB and KMT assert that their organisation Kid Risk holds a cooperative agreement with the US Centers for Disease Control and Prevention and holds grants from the Gates Foundation. MJF asserts that his employer, Penn State University, is a subrecipient of funds from Imperial College London for a grant from Gavi and that he holds grants from the Gates Foundation and the US National Science Foundation. MJ asserts that his employer, London School of Hygiene & Tropical Medicine, receives funding from the UK National Institute of Health Research, RCUK; the Gates Foundation; Gavi; the Wellcome Trust; WHO; the European Commission; the US Centers for Disease Control and Prevention; the Hong Kong Special Administrative Region Government; and the Task Force for Global Health. RAH and SPS assert that their employer, University of Cape Town, receives grant funding from the African Field Epidemiology Network and the US Centers for Disease Control and Prevention. RGW asserts that he receives funding from the Wellcome Trust (grant numbers 218261/Z/19/Z), National Institutes of Health (1R01AI147321-01, G-202303-69963, and R-202309-71190), European and Developing Countries Clinical Trials Partnership (RIA208D-2505B), UK Medical Research Council (CCF17-7779 via SET Bloomsbury), UK Economic and Social Research Council (ES/P008011/1), Bill & Melinda Gates Foundation (INV-004737 and INV-035506), and WHO (2020/985800-0). AL, KLO-B, NB-Z, PL, RCWH, and SYS work for WHO. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: The Immunization Agenda 2030 (IA2030) Impact Goal 1.1. aims to reduce the number of future deaths averted through immunization in the next decade. To estimate the potential impact of the aspirational coverage targets for IA2030, we developed an analytical framework and estimated the number of deaths averted due to an ambitious vaccination coverage scenario from 2021 to 2030 in 194 countries., Method: A demographic model was used to determine annual age-specific mortality estimates associated with vaccine coverage rates. For ten pathogens (Hepatitis B virus, Haemophilus influenzae type B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, Streptococcus pneumoniae, rotavirus, rubella, yellow fever), we derived single measures of country-, age-, and pathogen-specific relative risk of deaths conditional upon coverage rates, leveraging the data from 18 modeling groups as part of the Vaccine Impact Model Consortium (VIMC) for 110 countries. We used a logistic regression model to extrapolate the relative risk estimates to countries that were not modeled by VIMC. For four pathogens (diphtheria, tetanus, pertussis and tuberculosis), we used estimates from the Global Burden of Disease 2019 study and existing literature on vaccine efficacy. A future scenario defining years of vaccine introduction and scale-up needed to reach aspirational targets was developed as an input to estimate the long-term impact of vaccination taking place from 2021 to 2030., Findings: Overall, an estimated 51.5 million (95 % CI: 44.0-63.2) deaths are expected to be averted due to vaccinations administered between the years 2021 and 2030. With immunization coverage projected to increase over 2021-2030 an average of 5.2 million per year (4.4-6.3) deaths will be averted annually, with 4.4 million (3.9-5.1) deaths be averted for the year 2021, gradually rising to 5.8 million (4.9-7.5) deaths averted in 2030. The largest proportion of deaths is attributed to Measles and Hepatitis B accounting for 18.8 million (17.8-20.0) and 14.0 million (11.5-16.9) of total deaths averted respectively., Interpretation: The results from this global analysis demonstrate the substantial potential mortality reductions achievable if the IA2030 targets are met by 2030. Deaths caused by vaccine preventable diseases disproportionately affect LMICs in the African region., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 World Health Organization. Published by Elsevier Ltd.. All rights reserved.)

Background: There have been declines in global immunisation coverage due to the COVID-19 pandemic. Recovery has begun but is geographically variable. This disruption has led to under-immunised cohorts and interrupted progress in reducing vaccine-preventable disease burden. There have, so far, been few studies of the effects of coverage disruption on vaccine effects. We aimed to quantify the effects of vaccine-coverage disruption on routine and campaign immunisation services, identify cohorts and regions that could particularly benefit from catch-up activities, and establish if losses in effect could be recovered., Methods: For this modelling study, we used modelling groups from the Vaccine Impact Modelling Consortium from 112 low-income and middle-income countries to estimate vaccine effect for 14 pathogens. One set of modelling estimates used vaccine-coverage data from 1937 to 2021 for a subset of vaccine-preventable, outbreak-prone or priority diseases (ie, measles, rubella, hepatitis B, human papillomavirus [HPV], meningitis A, and yellow fever) to examine mitigation measures, hereafter referred to as recovery runs. The second set of estimates were conducted with vaccine-coverage data from 1937 to 2020, used to calculate effect ratios (ie, the burden averted per dose) for all 14 included vaccines and diseases, hereafter referred to as full runs. Both runs were modelled from Jan 1, 2000, to Dec 31, 2100. Countries were included if they were in the Gavi, the Vaccine Alliance portfolio; had notable burden; or had notable strategic vaccination activities. These countries represented the majority of global vaccine-preventable disease burden. Vaccine coverage was informed by historical estimates from WHO-UNICEF Estimates of National Immunization Coverage and the immunisation repository of WHO for data up to and including 2021. From 2022 onwards, we estimated coverage on the basis of guidance about campaign frequency, non-linear assumptions about the recovery of routine immunisation to pre-disruption magnitude, and 2030 endpoints informed by the WHO Immunization Agenda 2030 aims and expert consultation. We examined three main scenarios: no disruption, baseline recovery, and baseline recovery and catch-up., Findings: We estimated that disruption to measles, rubella, HPV, hepatitis B, meningitis A, and yellow fever vaccination could lead to 49 119 additional deaths (95% credible interval [CrI] 17 248-134 941) during calendar years 2020-30, largely due to measles. For years of vaccination 2020-30 for all 14 pathogens, disruption could lead to a 2·66% (95% CrI 2·52-2·81) reduction in long-term effect from 37 378 194 deaths averted (34 450 249-40 241 202) to 36 410 559 deaths averted (33 515 397-39 241 799). We estimated that catch-up activities could avert 78·9% (40·4-151·4) of excess deaths between calendar years 2023 and 2030 (ie, 18 900 [7037-60 223] of 25 356 [9859-75 073])., Interpretation: Our results highlight the importance of the timing of catch-up activities, considering estimated burden to improve vaccine coverage in affected cohorts. We estimated that mitigation measures for measles and yellow fever were particularly effective at reducing excess burden in the short term. Additionally, the high long-term effect of HPV vaccine as an important cervical-cancer prevention tool warrants continued immunisation efforts after disruption., Funding: The Vaccine Impact Modelling Consortium, funded by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation., Translations: For the Arabic, Chinese, French, Portguese and Spanish translations of the abstract see Supplementary Materials section., Competing Interests: Declaration of interests A-MH, XL, SE-L, JR, KA, MA, MJdV, MJF, KF, HF, TH, MJ, AK, SMM, SN, TP, TAP, AP, QTM, EV, AKW, HB, CC, HEC, AD, SH, JH, KJ, CK, J-HK, JK, BAL, VEP, YT, KW, NMF, CLT, and KAMG received funding from Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation via the Vaccine Impact Modelling Consortium (VIMC) during the study. A-MH, JR, SE-L, XL, SN, MJdV, TH, WH, KW, NMF, CLT, and KAMG receive funding from the Medical Research Council Centre for Global Infectious Disease Analysis (reference MR/R015600/1), which is jointly funded by the UK Medical Research Council and the UK Foreign, Commonwealth, and Development Office under a concordant agreement, and is also part of the European and Developing Countries Clinical Trials Partnership programme supported by the EU. A-MH, JR, SE-L, XL, SN, MJdV, TH, WH, KW, NMF, CLT, and KAMG receive funding from Community Jameel. A-MH is supported by the German Federal Ministry of Education and Research (grant 01LN2210A) and declares stock options in BIONTECH. CLT received payment for advice from GlaxoSmithKline. KA is supported by the Japan Agency for Medical Research and Development (JP223fa627004). KAMG received a speaker fee from Sanofi Pasteur. SN receives consulting fees from WHO. VEP is a member of the WHO Immunization and Implementation Research Advisory Committee. BAL receives personal fees from Epidemiologic Research and Methods and Hillevax. SMM receives consultant fees from Emergent Biosolutions. NMF receives grant funding from Janssen Pharmaceuticals, UK Research and Innovation, and the UK National Institute for Health and Care Research; declares consulting fees from the World Bank, WHO, and Gavi; receives travel expenses for WHO meetings; was on an advisory board for Takeda; and is a senior editor for eLife. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Reducing Ebola virus transmission relies on the ability to identify cases and limit contact with infected bodily fluids through biosecurity, safe sex practices, safe burial and vaccination. Armed conflicts can complicate outbreak detection and interventions due to widespread disruption to governments and populations. Guinea and the Democratic Republic of the Congo (DRC) have historically reported the largest and the most recent Ebola virus outbreaks. Understanding if conflict played a role in these outbreaks may help in identifying key risks factors to improve disease control., Methods: We used data from a range of publicly available data sources for both Ebola virus cases and conflict events from 2018 to 2021 in Guinea and the DRC. We fitted these data to conditional logistic regression models using the Self-Controlled Case Series methodology to evaluate the magnitude in which conflict increased the risk of reported Ebola virus cases in terms of incidence rate ratio. We re-ran the analysis sub-nationally, by conflict sub-event type and tested any lagged effects., Results: Conflict was significantly associated with an increased risk of reported Ebola virus cases in both the DRC and Guinea in recent outbreaks. The effect was of a similar magnitude at 1.88- and 1.98-times increased risk for the DRC and Guinea, respectively. The greatest effects (often higher than the national values) were found in many conflict prone areas and during protest/riot-related conflict events. Conflict was influential in terms of Ebola virus risk from 1 week following the event and remained important by 10 weeks., Conclusion: Extra vigilance is needed following protests and riot-related conflict events in terms of Ebola virus transmission. These events are highly disruptive, in terms of access to transportation and healthcare and are often in urban areas with high population densities. Additional public health messaging around these types of conflict events, relating to the risks and clinical symptoms may be helpful in reducing transmission. Future work should aim to further understand and quantify conflict severity and intensity, to evaluate dose-response relationships in terms of disease risk., (© 2024. The Author(s).)

Background: Malaysia introduced the two dose measles-mumps-rubella (MMR) vaccine in 2004 as part of its measles elimination strategy. However, despite high historical coverage of MCV1 and MCV2, Malaysia continues to report high measles incidence. This study suggests a novel indicator for investigating population immunity against measles in the Malaysian population., Methods: We define effective vaccine coverage (EVC) of measles as the proportion of a population vaccinated with measles-containing vaccine (MCV) and effectively protected against measles infection. A quantitative evaluation of EVC throughout the life course of Malaysian birth cohorts was conducted accounting for both vaccine efficacy (VE) and between-dose correlation (BdC). Measles vaccination coverage was sourced from WHO-UNICEF estimates of Malaysia's routine immunisation coverage and supplementary immunisation activities (SIAs). United Nations World population estimates and projections (UNWPP) provided birth cohort sizes stratified by age and year. A step wise joint Bernoulli distribution was used to proportionate the Malaysian population born between 1982, the first year of Malaysia's measles vaccination programme, and 2021, into individuals who received zero dose, one dose and multiple doses of MCV. VE estimates by age and doses received are then adopted to derive EVC. A sensitivity analysis was conducted using 1000 random combinations of BdC and VE parameters., Results: This study suggests that no birth cohort in the Malaysian population has achieved > 95% population immunity (EVC) conferred through measles vaccination since the measles immunisation programme began in Malaysia., Conclusion: The persistence of measles in Malaysia is due to pockets of insufficient vaccination coverage against measles in the population. Monitoring BdC through immunisation surveillance systems may allow for the identification of susceptible subpopulations (primarily zero-dose MCV individuals) and increase the coverage of individuals who are vaccinated with multiple doses of MCV. This study provides a tool for assessment of national-level population immunity of measles conferred through vaccination and does not consider subnational heterogeneity or vaccine waning. This tool can be readily applied to other regions and vaccine-preventable diseases., (© 2023. The Author(s).)

Background: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used., Methods: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.)., Results: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures., Conclusions: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping., (© 2023. BioMed Central Ltd., part of Springer Nature.)

A better understanding of population-level factors related to measles case fatality is needed to estimate measles mortality burden and impact of interventions such as vaccination. This study aimed to develop a conceptual framework of mechanisms associated with measles case fatality ratios (CFRs) and assess the scope of evidence available for related indicators. Using expert consultation, we developed a conceptual framework of mechanisms associated with measles CFR and identified population-level indicators potentially associated with each mechanism. We conducted a literature review by searching PubMed on 31 October 2021 to determine the scope of evidence for the expert-identified indicators. Studies were included if they contained evidence of an association between an indicator and CFR and were excluded if they were from non-human studies or reported non-original data. Included studies were assessed for study quality. Expert consultation identified five mechanisms in a conceptual framework of factors related to measles CFR. We identified 3772 studies for review and found 49 studies showing at least one significant association with CFR for 15 indicators (average household size, educational attainment, first- and second-dose coverage of measles-containing vaccine, human immunodeficiency virus prevalence, level of health care available, stunting prevalence, surrounding conflict, travel time to major city or settlement, travel time to nearest health care facility, under-five mortality rate, underweight prevalence, vitamin A deficiency prevalence, vitamin A treatment, and general malnutrition) and only non-significant associations for five indicators (antibiotic use for measles-related pneumonia, malaria prevalence, percent living in urban settings, pneumococcal conjugate vaccination coverage, vitamin A supplementation). Our study used expert consultation and a literature review to provide additional insights and a summary of the available evidence of these underlying mechanisms and indicators that could inform future measles CFR estimations.

As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.3 (95% credible interval (CrI) 7.7-8.8). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.9%, 95% CrI 2.7-3.2), followed by Delta (2.2%, 95% CrI 2.0-2.4), Wildtype (1.2%, 95% CrI 1.1-1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes., (© 2023. The Author(s).)

In an emergency epidemic response, data providers supply data on a best-faith effort to modellers and analysts who are typically the end user of data collected for other primary purposes such as to inform patient care. Thus, modellers who analyse secondary data have limited ability to influence what is captured. During an emergency response, models themselves are often under constant development and require both stability in their data inputs and flexibility to incorporate new inputs as novel data sources become available. This dynamic landscape is challenging to work with. Here we outline a data pipeline used in the ongoing COVID-19 response in the UK that aims to address these issues. A data pipeline is a sequence of steps to carry the raw data through to a processed and useable model input, along with the appropriate metadata and context. In ours, each data type had an individual processing report, designed to produce outputs that could be easily combined and used downstream. Automated checks were in-built and added as new pathologies emerged. These cleaned outputs were collated at different geographic levels to provide standardised datasets. Finally, a human validation step was an essential component of the analysis pathway and permitted more nuanced issues to be captured. This framework allowed the pipeline to grow in complexity and volume and facilitated the diverse range of modelling approaches employed by researchers. Additionally, every report or modelling output could be traced back to the specific data version that informed it ensuring reproducibility of results. Our approach has been used to facilitate fast-paced analysis and has evolved over time. Our framework and its aspirations are applicable to many settings beyond COVID-19 data, for example for other outbreaks such as Ebola, or where routine and regular analyses are required., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Understanding and continually assessing the achievability of global health targets is key to reducing disease burden and mortality. The Global Task Force on Cholera Control (GTFCC) Roadmap aims to reduce cholera deaths by 90% and eliminate the disease in twenty countries by 2030. The Roadmap has three axes focusing on reporting, response and coordination. Here, we assess the achievability of the GTFCC targets in Nigeria and identify where the three axes could be strengthened to reach and exceed these goals., Methodology/principal Findings: Using cholera surveillance data from Nigeria, cholera incidence was calculated and used to model time-varying reproduction number (R). A best fit random forest model was identified using R as the outcome variable and several environmental and social covariates were considered in the model, using random forest variable importance and correlation clustering. Future scenarios were created (based on varying degrees of socioeconomic development and emissions reductions) and used to project future cholera transmission, nationally and sub-nationally to 2070. The projections suggest that significant reductions in cholera cases could be achieved by 2030, particularly in the more developed southern states, but increases in cases remain a possibility. Meeting the 2030 target, nationally, currently looks unlikely and we propose a new 2050 target focusing on reducing regional inequities, while still advocating for cholera elimination being achieved as soon as possible., Conclusion/significance: The 2030 targets could potentially be reached by 2030 in some parts of Nigeria, but more effort is needed to reach these targets at a national level, particularly through access and incentives to cholera testing, sanitation expansion, poverty alleviation and urban planning. The results highlight the importance of and how modelling studies can be used to inform cholera policy and the potential for this to be applied in other contexts., Competing Interests: The authors declare no competing interests., (Copyright: © 2023 Charnley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3 weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the SARS-CoV-2 alpha variant prompted the UK to extend the interval between doses to 12 weeks. In this study, we aimed to quantify the effect of delaying the second vaccine dose in England., Methods: We used a previously described model of SARS-CoV-2 transmission, calibrated to COVID-19 surveillance data from England, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data, using a Bayesian evidence-synthesis framework. We modelled and compared the epidemic trajectory in the counterfactual scenario in which vaccine doses were administered 3 weeks apart against the real reported vaccine roll-out schedule of 12 weeks. We estimated and compared the resulting numbers of daily infections, hospital admissions, and deaths. In sensitivity analyses, we investigated scenarios spanning a range of vaccine effectiveness and waning assumptions., Findings: In the period from Dec 8, 2020, to Sept 13, 2021, the number of individuals who received a first vaccine dose was higher under the 12-week strategy than the 3-week strategy. For this period, we estimated that delaying the interval between the first and second COVID-19 vaccine doses from 3 to 12 weeks averted a median (calculated as the median of the posterior sample) of 58 000 COVID-19 hospital admissions (291 000 cumulative hospitalisations [95% credible interval 275 000-319 000] under the 3-week strategy vs 233 000 [229 000-238 000] under the 12-week strategy) and 10 100 deaths (64 800 deaths [60 200-68 900] vs 54 700 [52 800-55 600]). Similarly, we estimated that the 3-week strategy would have resulted in more infections compared with the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. In results by age group, the 12-week strategy led to more hospitalisations and deaths in older people in spring 2021, but fewer following the emergence of the delta variant during summer 2021., Interpretation: England's delayed-second-dose vaccination strategy was informed by early real-world data on vaccine effectiveness in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single-dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths overall., Funding: UK National Institute for Health Research; UK Medical Research Council; Community Jameel; Wellcome Trust; UK Foreign, Commonwealth and Development Office; Australian National Health and Medical Research Council; and EU., Competing Interests: Declaration of interests AC has received payment from Pfizer for teaching of mathematical modelling of infectious diseases. KAMG has received honoraria from Wellcome Genome Campus for lectures. LKW has received consultancy payments from the Wellcome Trust. ABH has received consultancy payments from WHO for COVID-19-related work; provides advice on COVID-19 modelling to the New South Wales Ministry of Health, Australia; and was previously engaged by Pfizer to advise on modelling of RSV vaccination strategies, for which she received no financial compensation. RS and NI are currently employed by the Wellcome Trust. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Nigeria currently reports the second highest number of cholera cases in Africa, with numerous socioeconomic and environmental risk factors. Less investigated are the role of extreme events, despite recent work showing their potential importance. To address this gap, we used a machine learning approach to understand the risks and thresholds for cholera outbreaks and extreme events, taking into consideration pre-existing vulnerabilities. We estimated time varying reproductive number (R) from cholera incidence in Nigeria and used a machine learning approach to evaluate its association with extreme events (conflict, flood, drought) and pre-existing vulnerabilities (poverty, sanitation, healthcare). We then created a traffic-light system for cholera outbreak risk, using three hypothetical traffic-light scenarios (Red, Amber and Green) and used this to predict R. The system highlighted potential extreme events and socioeconomic thresholds for outbreaks to occur. We found that reducing poverty and increasing access to sanitation lessened vulnerability to increased cholera risk caused by extreme events (monthly conflicts and the Palmers Drought Severity Index). The main limitation is the underreporting of cholera globally and the potential number of cholera cases missed in the data used here. Increasing access to sanitation and decreasing poverty reduced the impact of extreme events in terms of cholera outbreak risk. The results here therefore add further evidence of the need for sustainable development for disaster prevention and mitigation and to improve health and quality of life., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Charnley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Cholera outbreaks contribute substantially to illness and death in low- and middle-income countries. Cholera outbreaks are associated with several social and environmental risk factors, and extreme conditions can act as catalysts. A social extreme known to be associated with infectious disease outbreaks is conflict, causing disruption to services, loss of income, and displacement. To determine the extent of this association, we used the self-controlled case-series method and found that conflict increased the risk for cholera in Nigeria by 3.6 times and in the Democratic Republic of the Congo by 2.6 times. We also found that 19.7% of cholera outbreaks in Nigeria and 12.3% of outbreaks in the Democratic Republic of the Congo were attributable to conflict. Our results highlight the value of providing rapid and sufficient assistance during conflict-associated cholera outbreaks and working toward conflict resolution and addressing preexisting vulnerabilities, such as poverty and access to healthcare.

Despite vaccination being one of the most effective public health interventions, there are persisting inequalities and inequities in immunisation. Understanding the differences in subnational vaccine impact can help improve delivery mechanisms and policy. We analyse subnational vaccination coverage of measles first-dose (MCV1) and estimate patterns of inequalities in impact, represented as deaths averted, across 45 countries in Africa. We also evaluate how much this impact would improve under more equitable vaccination coverage scenarios. Using coverage data for MCV1 from 2000-2019, we estimate the number of deaths averted at the first administrative level. We use the ratio of deaths averted per vaccination from two mathematical models to extrapolate the impact at a subnational level. Next, we calculate inequality for each country, measuring the spread of deaths averted across its regions, accounting for differences in population. Finally, using three more equitable vaccination coverage scenarios, we evaluate how much impact of MCV1 immunisation could improve by (1) assuming all regions in a country have at least national coverage, (2) assuming all regions have the observed maximum coverage; and (3) assuming all regions have at least 80% coverage. Our results show that progress in coverage and reducing inequality has slowed in the last decade in many African countries. Under the three scenarios, a significant number of additional deaths in children could be prevented each year; for example, under the observed maximum coverage scenario, global MCV1 coverage would improve from 76% to 90%, resulting in a further 363(95%CrI:299-482) deaths averted per 100,000 live births. This paper illustrates that estimates of the impact of MCV1 immunisation at a national level can mask subnational heterogeneity. We further show that a considerable number of deaths could be prevented by maximising equitable access in countries with high inequality when increasing the global coverage of MCV1 vaccination., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Understanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data., Methods: We conducted descriptive analyses based on information systematically extracted from individual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, R t , correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs., Results: The corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: ± 2 days). Early transmission was driven primarily by returning travellers with R t peaking at 2.4 (95% CrI: 1.6, 3.3) nationally. In the final week of the trusted period (16-23 March 2020), R t accounting for importations diverged from overall R t at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6), respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age with individuals more likely to infect, and be infected by, contacts in a similar age group to them. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients < 20 years old developing pneumonia or severe respiratory symptoms., Conclusions: Information collected in the early phases of an outbreak are important in characterising any novel pathogen. The availability of timely and detailed data and appropriate analyses is critical to estimate and understand a pathogen's transmissibility, high-risk settings for transmission, and key symptoms. These insights can help to inform urgent response strategies., (© 2022. The Author(s).)

Background: Vibrio cholerae is a water-borne pathogen with a global burden estimate at 1.4 to 4.0 million annual cases. Over 94% of these cases are reported in Africa and more research is needed to understand cholera dynamics in the region. Cholera data are lacking, mainly due to reporting issues, creating barriers for widespread research on cholera epidemiology and management in Africa., Main Body: Here, we present datasets that were created to help address this gap, collating freely available sub-national cholera data for Nigeria and the Democratic Republic of Congo. The data were collated from a variety of English and French publicly available sources, including the World Health Organization, PubMed, UNICEF, EM-DAT, the Nigerian CDC and peer-reviewed literature. These data include information on cases, deaths, age, gender, oral cholera vaccination, risk factors and interventions., Conclusion: These datasets can facilitate qualitative, quantitative and mixed methods research in these two high burden countries to assist in public health planning. The data can be used in collaboration with organisations in the two countries, which have also collected data or undertaking research. By making the data and methods available, we aim to encourage their use and further data collection and compilation to help improve the data gaps for cholera in Africa., (© 2022. The Author(s).)

Background: Temperature and precipitation are known to affect Vibrio cholerae outbreaks. Despite this, the impact of drought on outbreaks has been largely understudied. Africa is both drought and cholera prone and more research is needed in Africa to understand cholera dynamics in relation to drought., Methods: Here, we analyse a range of environmental and socioeconomic covariates and fit generalised linear models to publicly available national data, to test for associations with several indices of drought and make cholera outbreak projections to 2070 under three scenarios of global change, reflecting varying trajectories of CO 2 emissions, socio-economic development, and population growth., Results: The best-fit model implies that drought is a significant risk factor for African cholera outbreaks, alongside positive effects of population, temperature and poverty and a negative effect of freshwater withdrawal. The projections show that following stringent emissions pathways and expanding sustainable development may reduce cholera outbreak occurrence in Africa, although these changes were spatially heterogeneous., Conclusions: Despite an effect of drought in explaining recent cholera outbreaks, future projections highlighted the potential for sustainable development gains to offset drought-related impacts on cholera risk. Future work should build on this research investigating the impacts of drought on cholera on a finer spatial scale and potential non-linear relationships, especially in high-burden countries which saw little cholera change in the scenario analysis., (© 2021. The Author(s).)

Background: England's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories., Methods: This mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions., Findings: The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69-83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500-5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700-1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness., Interpretation: Our findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures., Funding: National Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office., Competing Interests: Declaration of interests AC has received payment from Pfizer for teaching of mathematical modelling of infectious diseases. KAMG has received honoraria from Wellcome Genome Campus for lectures and salary support from the Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance, through Imperial College London for work outside this study. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Deaths due to vaccine preventable diseases cause a notable proportion of mortality worldwide. To quantify the importance of vaccination, it is necessary to estimate the burden averted through vaccination. The Vaccine Impact Modelling Consortium (VIMC) was established to estimate the health impact of vaccination., Methods: We describe the methods implemented by the VIMC to estimate impact by calendar year, birth year and year of vaccination (YoV). The calendar and birth year methods estimate impact in a particular year and over the lifetime of a particular birth cohort, respectively. The YoV method estimates the impact of a particular year's vaccination activities through the use of impact ratios which have no stratification and stratification by activity type and/or birth cohort. Furthermore, we detail an impact extrapolation (IE) method for use between coverage scenarios. We compare the methods, focusing on YoV for hepatitis B, measles and yellow fever., Results: We find that the YoV methods estimate similar impact with routine vaccinations but have greater yearly variation when campaigns occur with the birth cohort stratification. The IE performs well for the YoV methods, providing a time-efficient mechanism for updates to impact estimates., Conclusions: These methods provide a robust set of approaches to quantify vaccination impact; however it is vital that the area of impact estimation continues to develop in order to capture the full effect of immunisation., (© 2021. The Author(s).)

The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others' policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country's first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic., (© 2021. The Author(s).)

We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional surveillance data for England. Compared with other approaches, our model provides a synthesis of multiple surveillance data streams into a single coherent modeling framework, allowing transmission and severity to be disentangled from features of the surveillance system. Of the control measures implemented, only national lockdown brought the reproduction number ( R t eff ) below 1 consistently; if introduced 1 week earlier, it could have reduced deaths in the first wave from an estimated 48,600 to 25,600 [95% credible interval (CrI): 15,900 to 38,400]. The infection fatality ratio decreased from 1.00% (95% CrI: 0.85 to 1.21%) to 0.79% (95% CrI: 0.63 to 0.99%), suggesting improved clinical care. The infection fatality ratio was higher in the elderly residing in care homes (23.3%, 95% CrI: 14.7 to 35.2%) than those residing in the community (7.9%, 95% CrI: 5.9 to 10.3%). On 2 December 2020, England was still far from herd immunity, with regional cumulative infection incidence between 7.6% (95% CrI: 5.4 to 10.2%) and 22.3% (95% CrI: 19.4 to 25.4%) of the population. Therefore, any vaccination campaign will need to achieve high coverage and a high degree of protection in vaccinated individuals to allow nonpharmaceutical interventions to be lifted without a resurgence of transmission., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

SARS-CoV-2 infections have been reported in all age groups including infants, children, and adolescents. However, the role of children in the COVID-19 pandemic is still uncertain. This systematic review of early studies synthesises evidence on the susceptibility of children to SARS-CoV-2 infection, the severity and clinical outcomes in children with SARS-CoV-2 infection, and the transmissibility of SARS-CoV-2 by children in the initial phases of the COVID-19 pandemic. A systematic literature review was conducted in PubMed. Reviewers extracted data from relevant, peer-reviewed studies published up to July 4th 2020 during the first wave of the SARS-CoV-2 outbreak using a standardised form and assessed quality using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. For studies included in the meta-analysis, we used a random effects model to calculate pooled estimates of the proportion of children considered asymptomatic or in a severe or critical state. We identified 2775 potential studies of which 128 studies met our inclusion criteria; data were extracted from 99, which were then quality assessed. Finally, 29 studies were considered for the meta-analysis that included information of symptoms and/or severity, these were further assessed based on patient recruitment. Our pooled estimate of the proportion of test positive children who were asymptomatic was 21.1% (95% CI: 14.0-28.1%), based on 13 included studies, and the proportion of children with severe or critical symptoms was 3.8% (95% CI: 1.5-6.0%), based on 14 included studies. We did not identify any studies designed to assess transmissibility in children and found that susceptibility to infection in children was highly variable across studies. Children's susceptibility to infection and onward transmissibility relative to adults is still unclear and varied widely between studies. However, it is evident that most children experience clinically mild disease or remain asymptomatically infected. More comprehensive contact-tracing studies combined with serosurveys are needed to quantify children's transmissibility relative to adults. With children back in schools, testing regimes and study protocols that will allow us to better understand the role of children in this pandemic are critical.

Yellow fever virus (YFV) is a zoonotic arbovirus affecting both humans and non-human primates (NHP's) in Africa and South America. Previous descriptions of YF's seasonality have relied purely on climatic explanations, despite the high proportion of cases occurring in people involved in agriculture. We use a series of random forest classification models to predict the monthly occurrence of YF in humans and NHP's across Brazil, by fitting four classes of covariates related to the seasonality of climate and agriculture (planting and harvesting), crop output and host demography. We find that models captured seasonal YF reporting in humans and NHPs when they considered seasonality of agriculture rather than climate, particularly for monthly aggregated reports. These findings illustrate the seasonality of exposure, through agriculture, as a component of zoonotic spillover. Additionally, by highlighting crop types and anthropogenic seasonality, these results could directly identify areas at highest risk of zoonotic spillover.

The worldwide endeavour to develop safe and effective COVID-19 vaccines has been extraordinary, and vaccination is now underway in many countries. However, the doses available in 2021 are likely to be limited. We extend a mathematical model of SARS-CoV-2 transmission across different country settings to evaluate the public health impact of potential vaccines using WHO-developed target product profiles. We identify optimal vaccine allocation strategies within- and between-countries to maximise averted deaths under constraints on dose supply. We find that the health impact of SARS-CoV-2 vaccination depends on the cumulative population-level infection incidence when vaccination begins, the duration of natural immunity, the trajectory of the epidemic prior to vaccination, and the level of healthcare available to effectively treat those with disease. Within a country we find that for a limited supply (doses for < 20% of the population) the optimal strategy is to target the elderly. However, with a larger supply, if vaccination can occur while other interventions are maintained, the optimal strategy switches to targeting key transmitters to indirectly protect the vulnerable. As supply increases, vaccines that reduce or block infection have a greater impact than those that prevent disease alone due to the indirect protection provided to high-risk groups. Given a 2 billion global dose supply in 2021, we find that a strategy in which doses are allocated to countries proportional to population size is close to optimal in averting deaths and aligns with the ethical principles agreed in pandemic preparedness planning., Competing Interests: Declaration of Competing Interest The authors declare grants from The Wellcome Trust (NMF, ACG), UK Medical Research Council (NMF, ACG, KH), National Institute for Health Research (NMF, KH), Community Jameel (NMF, KH), the UK Foreign, Commonwealth and Development Office (OJW) and the Bill and Melinda Gates Foundation (NMF), during the conduct of the study; grants from the Bill and Melinda Gates Foundation (ACG), National Institute for Health (ACG), GlaxoSmithKline (AL), and Gavi, the Vaccine Alliance (ACG, KAMG) outside the submitted work; personal fees from the World Health Organization (ACG, ABH, MB, PW) during the conduct of the study, in relation to developing the online interface (approximately £1000 per individual); and personal fees from The Global Fund (ACG, PW) outside the submitted work. ABH was previously engaged by Pfizer Inc to advise on modelling RSV vaccination strategies for which she received no financial compensation. There are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Infectious disease outbreaks are increasingly recognised as events that exacerbate impacts or prolong recovery following disasters. Yet, our understanding of the frequency, geography, characteristics and risk factors of post-disaster disease outbreaks globally is lacking. This limits the extent to which disease outbreak risks can be prepared for, monitored and responded to following disasters. Here, we conducted a global systematic review of post-disaster outbreaks and found that outbreaks linked to conflicts and hydrological events were most frequently reported, and most often caused by bacterial and water-borne agents. Lack of adequate WASH facilities and poor housing were commonly reported risk factors. Displacement, through infrastructure damage, can lead to risk cascades for disease outbreaks; however, displacement can also be an opportunity to remove people from danger and ultimately protect health. The results shed new light on post-disaster disease outbreaks and their risks. Understanding these risk factors and cascades, could help improve future region-specific disaster risk reduction.

Background: The Eliminate Yellow fever Epidemics (EYE) strategy was launched in 2017 in response to the resurgence of yellow fever in Africa and the Americas. The strategy relies on several vaccination activities, including preventive mass vaccination campaigns (PMVCs). However, to what extent PMVCs are associated with a decreased risk of outbreak has not yet been quantified., Methods and Findings: We used the self-controlled case series (SCCS) method to assess the association between the occurrence of yellow fever outbreaks and the implementation of PMVCs at the province level in the African endemic region. As all time-invariant confounders are implicitly controlled for in the SCCS method, this method is an alternative to classical cohort or case-control study designs when the risk of residual confounding is high, in particular confounding by indication. The locations and dates of outbreaks were identified from international epidemiological records, and information on PMVCs was provided by coordinators of vaccination activities and international funders. The study sample consisted of provinces that were both affected by an outbreak and targeted for a PMVC between 2005 and 2018. We compared the incidence of outbreaks before and after the implementation of a PMVC. The sensitivity of our estimates to a range of assumptions was explored, and the results of the SCCS method were compared to those obtained through a retrospective cohort study design. We further derived the number of yellow fever outbreaks that have been prevented by PMVCs. The study sample consisted of 33 provinces from 11 African countries. Among these, the first outbreak occurred during the pre-PMVC period in 26 (79%) provinces, and during the post-PMVC period in 7 (21%) provinces. At the province level, the post-PMVC period was associated with an 86% reduction (95% CI 66% to 94%, p < 0.001) in the risk of outbreak as compared to the pre-PMVC period. This negative association between exposure to PMVCs and outbreak was robustly observed across a range of sensitivity analyses, especially when using quantitative estimates of vaccination coverage as an alternative exposure measure, or when varying the observation period. In contrast, the results of the cohort-style analyses were highly sensitive to the choice of covariates included in the model. Based on the SCCS results, we estimated that PMVCs were associated with a 34% (95% CI 22% to 45%) reduction in the number of outbreaks in Africa from 2005 to 2018. A limitation of our study is the fact that it does not account for potential time-varying confounders, such as changing environmental drivers of yellow fever and possibly improved disease surveillance., Conclusions: In this study, we provide new empirical evidence of the high preventive impact of PMVCs on yellow fever outbreaks. This study illustrates that the SCCS method can be advantageously applied at the population level in order to evaluate a public health intervention., Competing Interests: Tini Garske was the principal investigator of a grant from The Bill & Melinda Gates Foundation, and the principal investigator of a grant from GAVI, both funding the Vaccine Impact Modelling Consortium. Other authors have declared no competing interests. Author Tini Garske was unable to confirm their authorship contributions. On their behalf, the corresponding author has reported their contributions to the best of their knowledge.

In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.

Background: The past two decades have seen expansion of childhood vaccination programmes in low-income and middle-income countries (LMICs). We quantify the health impact of these programmes by estimating the deaths and disability-adjusted life-years (DALYs) averted by vaccination against ten pathogens in 98 LMICs between 2000 and 2030., Methods: 16 independent research groups provided model-based disease burden estimates under a range of vaccination coverage scenarios for ten pathogens: hepatitis B virus, Haemophilus influenzae type B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, Streptococcus pneumoniae, rotavirus, rubella, and yellow fever. Using standardised demographic data and vaccine coverage, the impact of vaccination programmes was determined by comparing model estimates from a no-vaccination counterfactual scenario with those from a reported and projected vaccination scenario. We present deaths and DALYs averted between 2000 and 2030 by calendar year and by annual birth cohort., Findings: We estimate that vaccination of the ten selected pathogens will have averted 69 million (95% credible interval 52-88) deaths between 2000 and 2030, of which 37 million (30-48) were averted between 2000 and 2019. From 2000 to 2019, this represents a 45% (36-58) reduction in deaths compared with the counterfactual scenario of no vaccination. Most of this impact is concentrated in a reduction in mortality among children younger than 5 years (57% reduction [52-66]), most notably from measles. Over the lifetime of birth cohorts born between 2000 and 2030, we predict that 120 million (93-150) deaths will be averted by vaccination, of which 58 million (39-76) are due to measles vaccination and 38 million (25-52) are due to hepatitis B vaccination. We estimate that increases in vaccine coverage and introductions of additional vaccines will result in a 72% (59-81) reduction in lifetime mortality in the 2019 birth cohort., Interpretation: Increases in vaccine coverage and the introduction of new vaccines into LMICs have had a major impact in reducing mortality. These public health gains are predicted to increase in coming decades if progress in increasing coverage is sustained., Funding: Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

In the last 20 years yellow fever (YF) has seen dramatic changes to its incidence and geographic extent, with the largest outbreaks in South America since 1940 occurring in the previously unaffected South-East Atlantic coast of Brazil in 2016-2019. While habitat fragmentation and land-cover have previously been implicated in zoonotic disease, their role in YF has not yet been examined. We examined the extent to which vegetation, land-cover, climate and host population predicted the numbers of months a location reported YF per year and by each month over the time-period. Two sets of models were assessed, one looking at interannual differences over the study period (2003-2016), and a seasonal model looking at intra-annual differences by month, averaging over the years of the study period. Each was fit using hierarchical negative-binomial regression in an exhaustive model fitting process. Within each set, the best performing models, as measured by the Akaike Information Criterion (AIC), were combined to create ensemble models to describe interannual and seasonal variation in YF. The models reproduced the spatiotemporal heterogeneities in YF transmission with coefficient of determination (R2) values of 0.43 (95% CI 0.41-0.45) for the interannual model and 0.66 (95% CI 0.64-0.67) for the seasonal model. For the interannual model, EVI, land-cover and vegetation heterogeneity were the primary contributors to the variance explained by the model, and for the seasonal model, EVI, day temperature and rainfall amplitude. Our models explain much of the spatiotemporal variation in YF in South America, both seasonally and across the period 2003-2016. Vegetation type (EVI), heterogeneity in vegetation (perhaps a proxy for habitat fragmentation) and land cover explain much of the trends in YF transmission seen. These findings may help understand the recent expansions of the YF endemic zone, as well as to the highly seasonal nature of YF., Competing Interests: The authors have declared that no competing interests exist.

Objectives: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China., Methods: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces., Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic., Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that R t was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.

Background: After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their "test, trace, isolate" strategy. However, it is important to understand the epidemiological peculiarities of South Korea's outbreak and characterise their response before attempting to emulate these measures elsewhere., Methods: We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, R t , using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources., Results: We estimated that after the initial rapid growth in cases, R t dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, R t was back below one where it remained until the end of our study (July 13th). Despite less stringent "lockdown" measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%., Conclusions: Whilst early adoption of testing and contact tracing is likely to be important for South Korea's successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.

Background: COVID-19 has the potential to cause substantial disruptions to health services, due to cases overburdening the health system or response measures limiting usual programmatic activities. We aimed to quantify the extent to which disruptions to services for HIV, tuberculosis, and malaria in low-income and middle-income countries with high burdens of these diseases could lead to additional loss of life over the next 5 years., Methods: Assuming a basic reproduction number of 3·0, we constructed four scenarios for possible responses to the COVID-19 pandemic: no action, mitigation for 6 months, suppression for 2 months, or suppression for 1 year. We used established transmission models of HIV, tuberculosis, and malaria to estimate the additional impact on health that could be caused in selected settings, either due to COVID-19 interventions limiting activities, or due to the high demand on the health system due to the COVID-19 pandemic., Findings: In high-burden settings, deaths due to HIV, tuberculosis, and malaria over 5 years could increase by up to 10%, 20%, and 36%, respectively, compared with if there was no COVID-19 pandemic. The greatest impact on HIV was estimated to be from interruption to antiretroviral therapy, which could occur during a period of high health system demand. For tuberculosis, the greatest impact would be from reductions in timely diagnosis and treatment of new cases, which could result from any prolonged period of COVID-19 suppression interventions. The greatest impact on malaria burden could be as a result of interruption of planned net campaigns. These disruptions could lead to a loss of life-years over 5 years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV and tuberculosis epidemics., Interpretation: Maintaining the most critical prevention activities and health-care services for HIV, tuberculosis, and malaria could substantially reduce the overall impact of the COVID-19 pandemic., Funding: Bill & Melinda Gates Foundation, Wellcome Trust, UK Department for International Development, and Medical Research Council., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

On 21 February 2020, a resident of the municipality of Vo', a small town near Padua (Italy), died of pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection 1 . This was the first coronavirus disease 19 (COVID-19)-related death detected in Italy since the detection of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province 2 . In response, the regional authorities imposed the lockdown of the whole municipality for 14 days 3 . Here we collected information on the demography, clinical presentation, hospitalization, contact network and the presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. From the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI): 2.1-3.3%). From the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI: 0.8-1.8%). Notably, 42.5% (95% CI: 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (that is, did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI: 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (P = 0.62 and 0.74 for E and RdRp genes, respectively, exact Wilcoxon-Mann-Whitney test). This study sheds light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides insights into its transmission dynamics and the efficacy of the implemented control measures.

The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower-income countries may reduce overall risk, but limited health system capacity coupled with closer intergenerational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower-income countries resulting from the poorer health care available. Of countries that have undertaken suppression to date, lower-income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being, and economies of these countries., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: To counter the increasing global risk of Yellow fever (YF), the World Health Organisation initiated the Eliminate Yellow fever Epidemics (EYE) strategy. Estimating YF burden, as well as vaccine impact, while accounting for the features of urban YF transmission such as indirect benefits of vaccination, is key to informing this strategy., Methods and Findings: We developed two model variants to estimate YF burden in sub-Saharan Africa, assuming all infections stem from either the sylvatic or the urban cycle of the disease. Both relied on an ecological niche model fitted to the local presence of any YF reported event in 34 African countries. We calibrated under-reporting using independent estimates of transmission intensity provided by 12 serological surveys performed in 11 countries. We calculated local numbers of YF infections, deaths and disability-adjusted life years (DALYs) lost based on estimated transmission intensity while accounting for time-varying vaccination coverage. We estimated vaccine demand and impact of future preventive mass vaccination campaigns (PMVCs) according to various vaccination scenarios. Vaccination activities conducted in Africa between 2005 and 2017 were estimated to prevent from 3.3 (95% CI 1.2-7.7) to 6.1 (95% CI 2.4-13.2) millions of deaths over the lifetime of vaccinees, representing extreme scenarios of none or maximal herd effects, respectively. By prioritizing provinces based on the risk of urban YF transmission in future PMVCs, an average of 37.7 million annual doses for PMVCs over eight years would avert an estimated 9,900,000 (95% CI 7,000,000-13,400,000) infections and 480,000 (180,000-1,140,000) deaths over the lifetime of vaccinees, corresponding to 1.7 (0.7-4.1) deaths averted per 1,000 vaccine doses., Conclusions: By estimating YF burden and vaccine impact over a range of spatial and temporal scales, while accounting for the specificity of urban transmission, our model can be used to inform the current EYE strategy., Competing Interests: KJ, AH and TG received personal fees from the World Health Organization for consultancy on the topic. All other authors declare no competing interest.

Background : Several non-pharmaceutical interventions (NPIs) have been implemented across the world to control the coronavirus disease (COVID-19) pandemic. Social distancing (SD) interventions applied so far have included school closures, remote working and quarantine. These measures have been shown to have large impacts on pandemic influenza transmission. However, there has been comparatively little examination of such measures for COVID-19. Methods : We examined the existing literature, and collated data, on implementation of NPIs to examine their effects on the COVID-19 pandemic so far. Data on NPIs were collected from official government websites as well as from media sources. Results : Measures such as travel restrictions have been implemented in multiple countries and appears to have slowed the geographic spread of COVID-19 and reduced initial case numbers. We find that, due to the relatively sparse information on the differences with and without interventions, it is difficult to quantitatively assess the efficacy of many interventions. Similarly, whilst the comparison to other pandemic diseases such as influenza can be helpful, there are key differences that could affect the efficacy of similar NPIs. Conclusions : The timely implementation of control measures is key to their success and must strike a balance between early enough application to reduce the peak of the epidemic and ensuring that they can be feasibly maintained for an appropriate duration. Such measures can have large societal impacts and they need to be appropriately justified to the population. As the pandemic of COVID-19 progresses, quantifying the impact of interventions will be a vital consideration for the appropriate use of mitigation strategies., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Imai N et al.)

Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model's assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model's evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to compare them with established modelling approaches to highlight uncertainty in structures and data., Competing Interests: The authors have declared that no competing interests exist.

Background: Norovirus is thought to be responsible for a fifth of all acute gastroenteritis cases globally each year. The population level transmission dynamics of this very common virus are still poorly understood, in part because illness is under-reported. With vaccines undergoing clinical trials, there is a growing need for appropriate, empirically grounded models, to predict the likely impact of vaccination., Methods: We developed a dynamic age-specific mathematical model of norovirus transmission and vaccination, informed by available data, particularly age-stratified time series case notification data. We introduce the use of a self-reporting Markov model to account for variation by age and over time in the statutory reporting of norovirus in Germany. We estimated the model using a sequential Monte Carlo particle filter. We then extended and applied our estimated model to investigate the potential impact of a range of immunisation strategies. We performed sensitivity analyses on the mode of vaccine action and other vaccine-related parameters., Results: We find that routine immunisation could reduce the incidence of norovirus by up to 70.5% even when those vaccines do not provide complete protection from disease. Furthermore, we find that the relative efficiency of alternative strategies targeting different age groups are dependant on the outcome we consider and are sensitive to assumptions on the mode of vaccine action. Strategies that target infants and toddler are more efficient in preventing infection but targeting older adults is preferable for preventing severe outcomes., Conclusions: Our model provides a robust estimate of a dynamic transmission model for norovirus at the population level. Vaccination may be an effective strategy in preventing disease but further work is required to ascertain norovirus vaccine efficacy, its mode of action and to estimate the cost-effectiveness of immunisation against norovirus., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)