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Background: The COVID-19 pandemic has underscored the beneficial impact of vaccines. It also highlighted the need for future investments to expedite an equitable vaccine distribution. The 100 Days Mission aims to develop and make available a new vaccine against a future pathogen with pandemic potential within 100 days of that pathogen threat being recognised. We assessed the value of this mission by estimating the impact that it could have had on the COVID-19 pandemic., Methods: Using a previously published model of SARS-CoV-2 transmission dynamics fitted to excess mortality during the COVID-19 pandemic, we projected scenarios for three different investment strategies: rapid development and manufacture of a vaccine, increasing manufacturing capacity to eliminate supply constraints, and strengthening health systems to enable faster vaccine roll-outs and global equity. Each scenario was compared against the observed COVID-19 pandemic to estimate the public health and health-economic impacts of each scenario., Findings: If countries implemented non-pharmaceutical interventions (NPIs) as they did historically, the 100 Days Mission could have averted an estimated 8·33 million deaths (95% credible interval [CrI] 7·70-8·68) globally, mostly in lower-middle income countries. This corresponds to a monetary saving of US$14·35 trillion (95% CrI 12·96-17·87) based on the value of statistical life-years saved. Investment in manufacturing and health systems further increases deaths averted to 11·01 million (95% CrI 10·60-11·49). Under an alternative scenario whereby NPIs are lifted earlier on the basis of vaccine coverage, the 100 Days Mission alone could have reduced restrictions by 12 600 days (95% CrI 12 300-13 100) globally while still averting 5·76 million deaths (95% CrI 4·91-6·81)., Interpretation: Our findings show the value of the 100 Days Mission and how these can be amplified through improvements in manufacturing and health systems equity. However, these investments must be enhanced by prioritising a more equitable global vaccine distribution., Funding: Schmidt Science Fellowship in partnership with the Rhodes Trust, WHO, UK Medical Research Council, Coalition for Epidemic Preparedness Innovations., Competing Interests: Declaration of interests The Coalition for Epidemic Preparedness Innovations (CEPI) funded the investigation into the impact of the 100 Days Mission. The authors maintained full freedom when designing the study and deciding on additional scenarios to explore. ACG has received personal consultancy fees from HSBC, GlaxoSmithKline, Sanofi, and WHO related to COVID-19 epidemiology and from The Global Fund to Fight AIDS, Tuberculosis and Malaria for work unrelated to COVID-19. ACG was previously a non-remunerated member of a scientific advisory board for Moderna and is a non-remunerated member of the scientific advisory board for the Coalition for Epidemic Preparedness; has received personal consultancy fees from WHO for work related to malaria. OJW also received personal consultancy fees from WHO and the Clinton Health Access Initiative for work related to malaria. ABH has received personal consultancy fees from WHO for work related to COVID-19, and grant funding for COVID-19 work from WHO and NSW Ministry of Health, Australia; and is a member of the WHO Immunization and Vaccines Related Implementation Research Advisory Committee. AAT developed the allocation algorithm in collaboration with COVAX through funding from the Bill & Melinda Gates Foundation. 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: In Africa, the first Plasmodium falciparum artemisinin partial resistance mutation, was Kelch13 (K13) 561H - detected and validated at appreciable frequency in Rwanda in 2014. Surveillance to better define the extent of the emergence in Rwanda and neighboring countries is critical., Methods: We used novel liquid blood drop preservation combined with pooled sequencing to provide cost-effective rapid assessment of resistance mutation frequencies at multiple collection sites across Rwanda and neighboring regions in Uganda, Tanzania, and the Democratic Republic of the Congo (DRC). Malaria-positive samples (n=5,465) from 39 health facilities collected between May 2022 and March 2023 were sequenced in 199 pools., Results: In Rwanda, K13 561H and 675V were detected in 90% and 65% of sites with an average frequency of 19.0% (0-54.5%) and 5.0% (0-35.5%), respectively. In Tanzania, 561H had high frequency in multiple sites. 561H appeared at 1.6% in Uganda. 561H was absent from the DRC, although 675V was seen at low frequency. Concerningly candidate mutations were observed: 441L, 449A, and 469F co-occurred with validated mutations suggesting they are arising under the same pressures. Other markers for decreased susceptibility to artemether-lumefantrine are common: P. falciparum multidrug resistance protein 1 N86 at 98.0% (63.3-100%) and 184F at 47.0% (0-94.3%) and P. falciparum chloroquine resistance transporter 76T at 14.7% (0-58.6%). Additionally, sulfadoxine-pyrimethamine-associated mutations show high frequencies., Conclusion: K13 mutations are rapidly expanding in the region further endangering control efforts with the potential of engendering partner drug resistance., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Competing Interests: We declare no competing interests.

The introduction of artemisinin combination therapies (ACTs) has significantly reduced the burden of Plasmodium falciparum malaria, yet the emergence of artemisinin partial resistance (ART-R) as well as partner drug resistance threatens these gains. Recent confirmations of prevalent de novo ART-R mutations in Africa, in particular in Rwanda, Uganda and Ethiopia, underscore the urgency of addressing this issue in Africa. Our objective is to characterise this evolving resistance landscape in Africa and understand the speed with which ART-R will continue to spread. We produce estimates of both ART-R and partner drug resistance by bringing together WHO, WWARN and MalariaGen Pf7k data on antimalarial resistance in combination with a literature review. We integrate these estimates within a mathematical modelling approach, aincorporating to estimate parameters known to impact the selection of ART-R for each malaria-endemic country and explore scenarios of ART-R spread and establishment. We identify 16 malaria-endemic countries in Africa to prioritise for surveillance and future deployment of alternative antimalarial strategies, based on ART-R reaching greater than 10% prevalence by 2040 under current malaria burden and effective-treatment coverage. If resistance continues to spread at current rates with no change in drug policy, we predict that partner drug resistance will emerge and the mean percentage of treatment failure across Africa will reach 30.74% by 2060 (parameter uncertainty range: 24.98% - 34.54%). This translates to an alarming number of treatment failures, with 52,980,600 absolute cases of treatment failure predicted in 2060 in Africa (parameter uncertainty range: 26,374,200 - 93,672,400) based on current effective treatment coverage. Our results provide a refined and updated prediction model for the emergence of ART-R to help guide antimalarial policy and prioritise future surveillance efforts and innovation in Africa. These results put into stark context the speed with which antimalarial resistance may spread in Africa if left unchecked, confirming the need for swift and decisive action in formulating antimalarial treatment policies focused on furthering malaria control and containing antimalarial resistance in Africa. The rise of artemisinin partial resistance (ART-R) and increasing partner drug tolerance by Plasmodium falciparum malaria in Africa threatens to undo malaria control efforts. Recent confirmations of de novo ART-R markers in Rwanda, Uganda, and Ethiopia highlight the urgent need to address this threat in Africa, where the vast majority of cases and deaths occur. This study characterises the resistance landscape and predicts the spread of antimalarial resistance across Africa. We estimate and map the current levels of resistance markers related to artemisinin and its partner drugs using WHO, WWARN, and MalariaGen Pf7k data. We combine these estimates with current malaria transmission and treatment data and use an established individual-based model of malaria resistance to simulate future resistance spread. We identify 16 African countries at highest risk of ART-R for prioritisation of enhanced surveillance and alternative antimalarial strategies. We project that, without policy changes, ART-R will exceed 10% in these regions by 2040. By 2060, if resistance spreads unchecked, we predict mean treatment failure rates will reach 30.74% (parameter uncertainty range: 24.98% - 34.54%) across Africa. This alarming spread of resistance is predicted to cause 52.98 million treatment failures (uncertainty range: 26.37 million - 93.67 million) in 2060. The impact of antimalarial resistance in Africa, if left unchecked, would hugely damage efforts to reduce malaria burden. Our results underscore the critical need for swift policy action to contain resistance and guide future surveillance and intervention efforts., Competing Interests: Declaration of Interests OJW received personal consultancy fees from Clinton Health Access Initiative related to artemisinin resistance modelling.

Background: Vaccines have reduced severe disease and death from Coronavirus Disease 2019 (COVID-19). However, with evidence of waning efficacy coupled with continued evolution of the virus, health programmes need to evaluate the requirement for regular booster doses, considering their impact and cost-effectiveness in the face of ongoing transmission and substantial infection-induced immunity., Methods and Findings: We developed a combined immunological-transmission model parameterised with data on transmissibility, severity, and vaccine effectiveness. We simulated Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission and vaccine rollout in characteristic global settings with different population age-structures, contact patterns, health system capacities, prior transmission, and vaccine uptake. We quantified the impact of future vaccine booster dose strategies with both ancestral and variant-adapted vaccine products, while considering the potential future emergence of new variants with modified transmission, immune escape, and severity properties. We found that regular boosting of the oldest age group (75+) is an efficient strategy, although large numbers of hospitalisations and deaths could be averted by extending vaccination to younger age groups. In countries with low vaccine coverage and high infection-derived immunity, boosting older at-risk groups was more effective than continuing primary vaccination into younger ages in our model. Our study is limited by uncertainty in key parameters, including the long-term durability of vaccine and infection-induced immunity as well as uncertainty in the future evolution of the virus., Conclusions: Our modelling suggests that regular boosting of the high-risk population remains an important tool to reduce morbidity and mortality from current and future SARS-CoV-2 variants. Our results suggest that focusing vaccination in the highest-risk cohorts will be the most efficient (and hence cost-effective) strategy to reduce morbidity and mortality., Competing Interests: ACG was previously a non-renumerated member of a scientific advisory board for Moderna, has received consultancy funding from GSK and Sanofi related to COVID-19 vaccination, and is a member of the CEPI scientific advisory board. She has received grant funding from Gavi for COVID-19 related work. ABH, PW and ACG have previously received consultancy payments from WHO for COVID-19 related work. ABH provides COVID-19 modelling advice to the New South Wales Ministry of Health, Australia. ABH was previously engaged by Pfizer Inc to advise on modelling RSV vaccination strategies for which she received no financial compensation. EMR is a non-remunerated member of the UK Vaccines Network, the UKRI COVID-19 taskforce and the British Society for Immunology Covid-19 taskforce. SLW is currently employed by Merck (USA/CAN), MSD (outside USA/CAN). None of this work was done while the author was an employee and none of his work for Merck is related in any way to the contents of this paper. NMF declares grants from UK MRC, UK NIHR, Bill and Melinda Gates Foundation, Gavi, Wellcome Trust. NMF is Member of UK government and WHO advisory committees., (Copyright: © 2023 Hogan 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.)

With the ongoing evolution of the SARS-CoV-2 virus updated vaccines may be needed. We fitted a model linking immunity levels and protection to vaccine effectiveness data from England for three vaccines (Oxford/AstraZeneca AZD1222, Pfizer-BioNTech BNT162b2, Moderna mRNA-1273) and two variants (Delta, Omicron). Our model reproduces the observed sustained protection against hospitalisation and death from the Omicron variant over the first six months following dose 3 with the ancestral vaccines but projects a gradual waning to moderate protection after 1 year. Switching the fourth dose to a variant-matched vaccine against Omicron BA.1/2 is projected to prevent nearly twice as many hospitalisations and deaths over a 1-year period compared to administering the ancestral vaccine. This result is sensitive to the degree to which immunogenicity data can be used to predict vaccine effectiveness and uncertainty regarding the impact that infection-induced immunity (not captured here) may play in modifying future vaccine effectiveness., (© 2023. The Author(s).)

Objectives: This study examined the treatment response of mixed vs single-species Plasmodium falciparum infections to artemisinin-based combination therapies (ACTs)., Methods: A total of 1211 blood samples collected on days 0, 7, 14, 21, 28, 35, and 42 from 173 individuals enrolled in two randomized ACT efficacy studies were tested for malaria using 18s ribosomal RNA-based real-time polymerase chain reaction. All recurrent parasitemia were characterized for Plasmodium species composition and time to reinfection during 42-day follow-up compared across ACTs., Results: Day 0 samples had 71.1% (116/163) single P. falciparum infections and 28.2% (46/163) coinfections. A total of 54.0% (88/163) of individuals tested positive for Plasmodium at least once between days 7-42. A total of 19.3% (17/88) of individuals with recurrent infections were infected with a different Plasmodium species than observed at day 0, with 76.5% (13/17) of these "hidden" infections appearing after clearing P. falciparum present at day 0. Artesunate-mefloquine (16.4 hours) and dihydroartemisinin-piperaquine (17.6 hours) had increased clearance rates over artemether-lumefantrine (21.0 hours). Dihydroartemisinin-piperaquine exhibited the longest duration of reinfection prophylaxis. Cure rates were comparable across each species composition., Conclusion: No differences in clearance rates were found depending on whether the infection contained species other than P. falciparum. Significantly longer durations of protection were observed for individuals treated with dihydroartemisinin-piperaquine., Competing Interests: Declaration of competing interests The authors have no competing interests to declare., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Reported COVID-19 cases and associated mortality remain low in many sub-Saharan countries relative to global averages, but true impact is difficult to estimate given limitations around surveillance and mortality registration. In Lusaka, Zambia, burial registration and SARS-CoV-2 prevalence data during 2020 allow estimation of excess mortality and transmission. Relative to pre-pandemic patterns, we estimate age-dependent mortality increases, totalling 3212 excess deaths (95% CrI: 2104-4591), representing an 18.5% (95% CrI: 13.0-25.2%) increase relative to pre-pandemic levels. Using a dynamical model-based inferential framework, we find that these mortality patterns and SARS-CoV-2 prevalence data are in agreement with established COVID-19 severity estimates. Our results support hypotheses that COVID-19 impact in Lusaka during 2020 was consistent with COVID-19 epidemics elsewhere, without requiring exceptional explanations for low reported figures. For more equitable decision-making during future pandemics, barriers to ascertaining attributable mortality in low-income settings must be addressed and factored into discourse around reported impact differences., (© 2023. The Author(s).)

Not all COVID-19 deaths are officially reported, and particularly in low-income and humanitarian settings, the magnitude of reporting gaps remains sparsely characterized. Alternative data sources, including burial site worker reports, satellite imagery of cemeteries, and social media-conducted surveys of infection may offer solutions. By merging these data with independently conducted, representative serological studies within a mathematical modeling framework, we aim to better understand the range of underreporting using examples from three major cities: Addis Ababa (Ethiopia), Aden (Yemen), and Khartoum (Sudan) during 2020. We estimate that 69 to 100%, 0.8 to 8.0%, and 3.0 to 6.0% of COVID-19 deaths were reported in each setting, respectively. In future epidemics, and in settings where vital registration systems are limited, using multiple alternative data sources could provide critically needed, improved estimates of epidemic impact. However, ultimately, these systems are needed to ensure that, in contrast to COVID-19, the impact of future pandemics or other drivers of mortality is reported and understood worldwide.

In malaria, individuals are often infected with different parasite strains. The complexity of infection (COI) is defined as the number of genetically distinct parasite strains in an individual. Changes in the mean COI in a population have been shown to be informative of changes in transmission intensity with a number of probabilistic likelihood and Bayesian models now developed to estimate the COI. However, rapid, direct measures based on heterozygosity or FwS do not properly represent the COI. In this work, we present two new methods that use easily calculated measures to directly estimate the COI from allele frequency data. Using a simulation framework, we show that our methods are computationally efficient and comparably accurate to current approaches in the literature. Through a sensitivity analysis, we characterize how the distribution of parasite densities, the assumed sequencing depth, and the number of sampled loci impact the bias and accuracy of our two methods. Using our developed methods, we further estimate the COI globally from Plasmodium falciparum sequencing data and compare the results against the literature. We show significant differences in the estimated COI globally between continents and a weak relationship between malaria prevalence and COI., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Paschalidis 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: Assessing the infectious reservoir is critical in malaria control and elimination strategies. We conducted a longitudinal epidemiological study in a high-malaria-burden region in Kenya to characterize transmission in an asymptomatic population., Methods: 488 study participants encompassing all ages in 120 households within 30 clusters were followed for 1 year with monthly sampling. Malaria was diagnosed by microscopy and molecular methods. Transmission potential in gametocytemic participants was assessed using direct skin and/or membrane mosquito feeding assays, then treated with artemether-lumefantrine. Study variables were assessed using mixed-effects generalized linear models., Results: Asexual and sexual parasite data were collected from 3792 participant visits, with 903 linked with feeding assays. Univariate analysis revealed that the 6-11-year-old age group was at higher risk of harboring asexual and sexual infections than those <6 years old (odds ratio [OR] 1.68, P < .001; and OR 1.81, P < .001), respectively. Participants with submicroscopic parasitemia were at a lower risk of gametocytemia compared with microscopic parasitemia (OR 0.04, P < .001), but they transmitted at a significantly higher rate (OR 2.00, P = .002). A large proportion of the study population who were infected at least once remained infected (despite treatment) with asexual (71.7%, 291/406) or sexual (37.4%, 152/406) parasites. 88.6% (365/412) of feeding assays conducted in individuals who failed treatment the previous month resulted in transmissions., Conclusions: Individuals with asymptomatic infection sustain the transmission cycle, with the 6-11-year age group serving as an important reservoir. The high rates of artemether-lumefantrine treatment failures suggest surveillance programs using molecular methods need to be expanded for accurate monitoring and evaluation of treatment outcomes., Competing Interests: Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Infectious Diseases Society of America.)

Background: Artemisinin-resistant genotypes of Plasmodium falciparum have now emerged a minimum of six times on three continents despite recommendations that all artemisinins be deployed as artemisinin combination therapies (ACTs). Widespread resistance to the non-artemisinin partner drugs in ACTs has the potential to limit the clinical and resistance benefits provided by combination therapy. We aimed to model and evaluate the long-term effects of high levels of partner-drug resistance on the early emergence of artemisinin-resistant genotypes., Methods: Using a consensus modelling approach, we used three individual-based mathematical models of Plasmodium falciparum transmission to evaluate the effects of pre-existing partner-drug resistance and ACT deployment on the evolution of artemisinin resistance. Each model simulates 100 000 individuals in a particular transmission setting (malaria prevalence of 1%, 5%, 10%, or 20%) with a daily time step that updates individuals' infection status, treatment status, immunity, genotype-specific parasite densities, and clinical state. We modelled varying access to antimalarial drugs if febrile (coverage of 20%, 40%, or 60%) with one primary ACT used as first-line therapy: dihydroartemisinin-piperaquine (DHA-PPQ), artesunate-amodiaquine (ASAQ), or artemether-lumefantrine (AL). The primary outcome was time until 0·25 580Y allele frequency for artemisinin resistance (the establishment time)., Findings: Higher frequencies of pre-existing partner-drug resistant genotypes lead to earlier establishment of artemisinin resistance. Across all models, a 10-fold increase in the frequency of partner-drug resistance genotypes on average corresponded to loss of artemisinin efficacy 2-12 years earlier. Most reductions in time to artemisinin resistance establishment were observed after an increase in frequency of the partner-drug resistance genotype from 0·0 to 0·10., Interpretation: Partner-drug resistance in ACTs facilitates the early emergence of artemisinin resistance and is a major public health concern. Higher-grade partner-drug resistance has the largest effect, with piperaquine resistance accelerating the early emergence of artemisinin-resistant alleles the most. Continued investment in molecular surveillance of partner-drug resistant genotypes to guide choice of first-line ACT is paramount., Funding: Schmidt Science Fellowship in partnership with the Rhodes Trust; Bill & Melinda Gates Foundation; Wellcome Trust., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 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 first COVID-19 vaccine outside a clinical trial setting was administered on Dec 8, 2020. To ensure global vaccine equity, vaccine targets were set by the COVID-19 Vaccines Global Access (COVAX) Facility and WHO. However, due to vaccine shortfalls, these targets were not achieved by the end of 2021. We aimed to quantify the global impact of the first year of COVID-19 vaccination programmes., Methods: A mathematical model of COVID-19 transmission and vaccination was separately fit to reported COVID-19 mortality and all-cause excess mortality in 185 countries and territories. The impact of COVID-19 vaccination programmes was determined by estimating the additional lives lost if no vaccines had been distributed. We also estimated the additional deaths that would have been averted had the vaccination coverage targets of 20% set by COVAX and 40% set by WHO been achieved by the end of 2021., Findings: Based on official reported COVID-19 deaths, we estimated that vaccinations prevented 14·4 million (95% credible interval [Crl] 13·7-15·9) deaths from COVID-19 in 185 countries and territories between Dec 8, 2020, and Dec 8, 2021. This estimate rose to 19·8 million (95% Crl 19·1-20·4) deaths from COVID-19 averted when we used excess deaths as an estimate of the true extent of the pandemic, representing a global reduction of 63% in total deaths (19·8 million of 31·4 million) during the first year of COVID-19 vaccination. In COVAX Advance Market Commitment countries, we estimated that 41% of excess mortality (7·4 million [95% Crl 6·8-7·7] of 17·9 million deaths) was averted. In low-income countries, we estimated that an additional 45% (95% CrI 42-49) of deaths could have been averted had the 20% vaccination coverage target set by COVAX been met by each country, and that an additional 111% (105-118) of deaths could have been averted had the 40% target set by WHO been met by each country by the end of 2021., Interpretation: COVID-19 vaccination has substantially altered the course of the pandemic, saving tens of millions of lives globally. However, inadequate access to vaccines in low-income countries has limited the impact in these settings, reinforcing the need for global vaccine equity and coverage., Funding: Schmidt Science Fellowship in partnership with the Rhodes Trust; WHO; UK Medical Research Council; Gavi, the Vaccine Alliance; Bill & Melinda Gates Foundation; National Institute for Health Research; and Community Jameel., Competing Interests: Declaration of interests ACG has received personal consultancy fees from HSBC, GlaxoSmithKline, and WHO related to COVID-19 epidemiology and from The Global Fund to Fight AIDS, Tuberculosis and Malaria for work unrelated to COVID-19. ACG is a non-remunerated member of scientific advisory boards for Moderna and the Coalition for Epidemic Preparedness. ABH and PW have received personal consultancy related to COVID-19 work from WHO. All other authors declare no competing interests., (Copyright © 2022 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 public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear., Methods: Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care., Results: The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics., Conclusions: Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.)

The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to reconstruct SARS-CoV-2 transmission dynamics and assess the level of underreporting in infections and deaths. Using weekly all-cause mortality data from Iran, we are able to show a strong agreement between our attack rate estimates and seroprevalence measurements in each province and find significant heterogeneity in the level of exposure across the country with 11 provinces reaching near 100% attack rates. Despite having a young population, our analysis reveals that incorporating limited access to medical services in our model, coupled with undercounting of COVID-19-related deaths, leads to estimates of infection fatality rate in most provinces of Iran that are comparable to high-income countries., (© 2022. The Author(s).)

Background: The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion., Methods: We built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries., Results: We demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49-2.53%., Conclusion: We developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics., Competing Interests: Competing interestsPGTW is an Editorial Board Member for Communications Medicine, but was not involved in the editorial review or peer review, nor in the decision to publish this article. The other authors have no competing interests to declare., (© The Author(s) 2022.)

India reported >10 million coronavirus disease (COVID-19) cases and 149,000 deaths in 2020. To reassess reported deaths and estimate incidence rates during the first 6 months of the epidemic, we used a severe acute respiratory syndrome coronavirus 2 transmission model fit to data from 3 serosurveys in Delhi and time-series documentation of reported deaths. We estimated 48.7% (95% credible interval 22.1%-76.8%) cumulative infection in the population through the end of September 2020. Using an age-adjusted overall infection fatality ratio based on age-specific estimates from mostly high-income countries, we estimated that just 15.0% (95% credible interval 9.3%-34.0%) of COVID-19 deaths had been reported, indicating either substantial underreporting or lower age-specific infection-fatality ratios in India than in high-income countries. Despite the estimated high attack rate, additional epidemic waves occurred in late 2020 and April-May 2021. Future dynamics will depend on the duration of natural and vaccine-induced immunity and their effectiveness against new variants.

Background: Vaccine hesitancy - a delay in acceptance or refusal of vaccines despite availability - has the potential to threaten the successful roll-out of SARS-CoV-2 vaccines globally. In this study, we aim to understand the likely impact of vaccine hesitancy on the control of the COVID-19 pandemic., Methods: We modelled the potential impact of vaccine hesitancy on the control of the pandemic and the relaxation of non-pharmaceutical interventions (NPIs) by combining an epidemiological model of SARS-CoV-2 transmission with data on vaccine hesitancy from population surveys., Results: Our simulations suggest that the mortality over a 2-year period could be up to 7.6 times higher in countries with high vaccine hesitancy compared to an ideal vaccination uptake if NPIs are relaxed. Alternatively, high vaccine hesitancy could prolong the need for NPIs to remain in place., Conclusions: While vaccination is an individual choice, vaccine-hesitant individuals have a substantial impact on the pandemic trajectory, which may challenge current efforts to control COVID-19. In order to prevent such outcomes, addressing vaccine hesitancy with behavioural interventions is an important priority in the control of the COVID-19 pandemic., Competing Interests: Competing interestsA.B.H., P.W. and A.C.G. declare consultancy fees from the World Health Organization in relation to modelling COVID-19 vaccine impact in the European region, outside the submitted work. The authors declare no other competing interests., (© The Author(s) 2022.)

While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings., Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings., Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made., Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions., Funding: This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1)., Competing Interests: AM, OW, OR, MM, AD, PD, MK, AK, RK, SK, KK, CL, Od, KL, WM, AM, CM, JM, EN, DN, WP, FR, SS, WW, RW, JW, JZ, CW No competing interests declared, PW received funding from WHO and the Asian Development Bank via their institution to investigate COVID19 Vaccine modelling. The author has no other competing interests to declare, MA has received research funding from Seqirus outside the submitted work, CG has received funding from NIH, CDC, AHRQ, FDA, Campbell Alliance/Syneos Health and Sanofi, outside the current work. CGG has also received consulting fees from Pfizer, Merck and Sanofi. The author has no other competing interests to declare, GP was employed by the Emmes Company while analyzing the Niakhar Senegal social contact network data included in this study. The Emmes Company was contracted to perform data cleaning and data analysis of the Niakhar, Senegal clinical trial data (but not the social contact network data) for this study before GEP joined the Emmes Company (in October 2015). After GEP joined the Emmes Company, the sole support from Emmes for this manuscript was in the form of salary support for GEP. GEP also received travel support from Thomas Francis Jr. Travel Fellowship. The author has no other competing interests to declare, JS received travel support for this work from Thomas Francis, Jr. Scholarship Fund. JDS has also received consultancy fees from the International Vaccine Institute, Seoul, Republic of Korea, for work related to studies of typhoid fever burden and vaccine impact. JDS also participates on a Data Safety Monitoring Board for optimising infant immunisation schedules in Uganda & Nepal, University of Oxford, UK (ongoing), PW received consultancy fees from Pfizer for a lecture on statistical inference using infectious disease models and payment or honoraria from IS Global, Barcelona (for a lecture on statistical inference using infectious disease models) and Lancet Global Health (for reviewing an article). The author has no other competing interests to declare

Competing Interests: Competing interests: We have read and understood BMJ policy on declaration of interests and have no conflicts of interest to declare.

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).)

Background: The coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on intensive care units (ICUs) in Europe. Ensuring access to care, irrespective of COVID-19 status, in winter 2020-2021 is essential., Methods: An integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients is used to estimate the demand for and resultant spare capacity of ICU beds, staff and ventilators under different epidemic scenarios in France, Germany and Italy across the 2020-2021 winter period. The effect of implementing lockdowns triggered by different numbers of COVID-19 patients in ICUs under varying levels of effectiveness is examined, using a 'dual-demand' (COVID-19 and non-COVID-19) patient model., Results: Without sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource. Reactive lockdowns could lead to large improvements in ICU capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and sustained under a higher level of suppression. The success of such interventions also depends on baseline bed numbers and average non-COVID-19 patient occupancy., Conclusion: Reductions in capacity deficits under different scenarios must be weighed against the feasibility and drawbacks of further lockdowns. Careful, continuous decision-making by national policymakers will be required across the winter period 2020-2021., (© The Author(s) 2021. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Reports of P. vivax infections among Duffy-negative hosts have accumulated throughout sub-Saharan Africa. Despite this growing body of evidence, no nationally representative epidemiological surveys of P. vivax in sub-Saharan Africa have been performed. To overcome this gap in knowledge, we screened over 17,000 adults in the Democratic Republic of the Congo (DRC) for P. vivax using samples from the 2013-2014 Demographic Health Survey. Overall, we found a 2.97% (95% CI: 2.28%, 3.65%) prevalence of P. vivax infections across the DRC. Infections were associated with few risk-factors and demonstrated a relatively flat distribution of prevalence across space with focal regions of relatively higher prevalence in the north and northeast. Mitochondrial genomes suggested that DRC P. vivax were distinct from circulating non-human ape strains and an ancestral European P. vivax strain, and instead may be part of a separate contemporary clade. Our findings suggest P. vivax is diffusely spread across the DRC at a low prevalence, which may be associated with long-term carriage of low parasitemia, frequent relapses, or a general pool of infections with limited forward propagation.

Background: Widespread social distancing and lockdowns of everyday activity have been the primary policy prescription across many countries throughout the coronavirus disease 2019 (COVID-19) pandemic. Despite their uniformity, these measures may be differentially valuable for different countries., Methods: We use a compartmental epidemiological model to project the spread of COVID-19 across policy scenarios in high- and low-income countries. We embed estimates of the welfare value of disease avoidance into the epidemiological projections to estimate the return to more stringent lockdown policies., Results: Social distancing measures that 'flatten the curve' of the disease provide immense welfare value in upper-income countries. However, social distancing policies deliver significantly less value in lower-income countries that have younger populations, which are less vulnerable to COVID-19. Equally important, social distancing mandates a trade-off between disease risk and economic activity. Poorer people are less able to make those economic sacrifices., Conclusions: The epidemiological and welfare value of social distancing is smaller in lower-income countries and such policies may exact a heavy toll on the poorest and most vulnerable. Workers in the informal sector often lack the resources and social protections that enable them to isolate themselves until the virus passes. By limiting these households' ability to earn a living, social distancing can lead to an increase in hunger, deprivation, and related mortality and morbidity., (© The Author(s) 2021. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.)

As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate ( R ). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention ("lockdown") and reductions in individuals' mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Background: As in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination campaign launches, cases and deaths are rising across the island., Methods: We used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine rollout., Results: C19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination campaign., Conclusions: Syndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact.

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.)

The COVID-19 pandemic has resulted in substantial mortality worldwide. However, to date, countries in the Middle East and Africa have reported considerably lower mortality rates than in Europe and the Americas. Motivated by reports of an overwhelmed health system, we estimate the likely under-ascertainment of COVID-19 mortality in Damascus, Syria. Using all-cause mortality data, we fit a mathematical model of COVID-19 transmission to reported mortality, estimating that 1.25% of COVID-19 deaths (sensitivity range 1.00% - 3.00%) have been reported as of 2 September 2020. By 2 September, we estimate that 4,380 (95% CI: 3,250 - 5,550) COVID-19 deaths in Damascus may have been missed, with 39.0% (95% CI: 32.5% - 45.0%) of the population in Damascus estimated to have been infected. Accounting for under-ascertainment corroborates reports of exceeded hospital bed capacity and is validated by community-uploaded obituary notifications, which confirm extensive unreported mortality in Damascus.

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

Background: The epidemiology and severity of non-falciparum malaria in endemic settings has garnered little attention. We aimed to characterise the prevalence, interaction, clinical risk factors, and temporal trends of non-falciparum Plasmodium species among symptomatic individuals presenting at health-care facilities in endemic settings of Kenya., Methods: We diagnosed and analysed infecting malaria species (Plasmodium falciparum, Plasmodium ovale curtisi, Plasmodium ovale wallikeri, and Plasmodium malariae) via PCR in clinical samples collected between March 1, 2008, and Dec 31, 2016, from six hospitals located in different regions of Kenya. We recruited patients aged 6 months or older who presented at outpatient departments with symptoms of malaria or tested positive for uncomplicated malaria by malaria rapid diagnostic test. Descriptive statistics were used to describe the prevalence and distribution of Plasmodium species. A statistical model was designed and used for estimating the frequency of Plasmodium species and assessing interspecies interactions. Mixed-effect linear regression models with random slopes for each location were used to test for change in prevalence over time., Findings: Samples from 2027 symptomatic participants presenting at care facilities were successfully analysed for all Plasmodium species. 1469 (72·5%) of the samples were P falciparum single-species infections, 523 (25·8%) were mixed infections, and only 35 (1·7%) were single non-falciparum species infections. 452 (22·3%) were mixed infections containing P ovale spp. A likelihood-based model calculation of the population frequency of each species estimated a significant within-host interference between P falciparum and P ovale curtisi. Mixed-effect logistic regression models identified a significant increase in P ovale wallikeri (2·1% per year; p=0·043) and P ovale curtisi (0·7% per year; p=0·0002) species over time, with a reciprocal decrease in P falciparum single-species infections (2·5% per year; p=0·0065). The frequency of P malariae infections did not significantly change over time. Risk of P falciparum infections presenting with fever was lower if co-infected with P malariae (adjusted odds ratio 0·43, 95% CI 0·25-0·74; p=0·0023)., Interpretation: Our results show a prevalence of non-falciparum species infections of 27·5% among symptomatic individuals presenting at care facilities, which is higher than expected from previous cross-sectional surveys. The proportion of infections with P ovale wallikeri and P ovale curtisi was observed to significantly increase over the period of study, which could be due to attenuated responsiveness of these species to malaria drug treatment. The increase in frequency of P ovale spp could threaten the malaria control efforts in Kenya and pose increased risk of malaria to travellers., Funding: Armed Forces Health Surveillance Branch and its Global Emerging Infections Surveillance Section., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

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.

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

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.)

High-throughput Plasmodium genomic data is increasingly useful in assessing prevalence of clinically important mutations and malaria transmission patterns. Understanding parasite diversity is important for identification of specific human or parasite populations that can be targeted by control programmes, and to monitor the spread of mutations associated with drug resistance. An up-to-date understanding of regional parasite population dynamics is also critical to monitor the impact of control efforts. However, this data is largely absent from high-burden nations in Africa, and to date, no such analysis has been conducted for malaria parasites in Tanzania countrywide. To this end, over 1,000 P. falciparum clinical isolates were collected in 2017 from 13 sites in seven administrative regions across Tanzania, and parasites were genotyped at 1,800 variable positions genome-wide using molecular inversion probes. Population structure was detectable among Tanzanian P. falciparum parasites, approximately separating parasites from the northern and southern districts and identifying genetically admixed populations in the north. Isolates from nearby districts were more likely to be genetically related compared to parasites sampled from more distant districts. Known drug resistance mutations were seen at increased frequency in northern districts (including two infections carrying pfk13-R561H), and additional variants with undetermined significance for antimalarial resistance also varied by geography. Malaria Indicator Survey (2017) data corresponded with genetic findings, including average region-level complexity-of-infection and malaria prevalence estimates. The parasite populations identified here provide important information on extant spatial patterns of genetic diversity of Tanzanian parasites, to which future surveys of genetic relatedness can be compared., (© 2020 John Wiley & Sons Ltd.)