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Estimates of the global health impact of immunisation are important for quantifying historical benefits as well as planning future investments and strategy. The Vaccine Impact Modelling Consortium (VIMC) was established in 2016 to provide reliable estimates of the health impact of immunisation. In this article we examine the consortium in its first five-year phase. We detail how vaccine impact was defined and the methods used to estimate it as well as the technical infrastructure required to underpin robust reproducibility of the outputs. We highlight some of the applications of estimates to date, how these were communicated and what their effect were. Finally, we explore some of the lessons learnt and remaining challenges for estimating the impact of vaccines and forming effective modelling consortia then discuss how this may be addressed in the second phase of VIMC. Modelled estimates are not a replacement for surveillance; however, they can examine theoretical counterfactuals and highlight data gaps to complement other activities. VIMC has implemented strategies to produce robust, standardised estimates of immunisation impact. But through the first phase of the consortium, critical lessons have been learnt both on the technical infrastructure and the effective engagement with modellers and stakeholders. To be successful, a productive dialogue with estimate consumers, producers and stakeholders needs to be underpinned by a rigorous and transparent analytical framework as well as an approach for building expertise in the short and long term., Competing Interests: Competing interests: KAMG, XL, HC, ED, RF, WH, DH, MJ, TM, TAP, AP, EV, KW, NMF, CLT, received funding from Gavi, BMGF and/or the Wellcome Trust via VIMC during the course of the study. The authors declare no other competing interests., (Copyright: © 2024 Gaythorpe KAM et al.)

The accuracy of crucial nuclear processes such as transcription, replication, and repair, depends on the local composition of chromatin and the regulatory proteins that reside there. Understanding these DNA-protein interactions at the level of specific genomic loci has remained challenging due to technical limitations. Here, we introduce a method termed "DNA O-MAP", which uses programmable peroxidase-conjugated oligonucleotide probes to biotinylate nearby proteins. We show that DNA O-MAP can be coupled with sample multiplexed quantitative proteomics and next-generation sequencing to quantify DNA-protein and DNA-DNA interactions at specific genomic loci., Competing Interests: Competing Interest Statement D.K.S. is a collaborator with Thermo Fisher Scientific, Genentech, Calico Labs, and AI Proteins. C.K.C., A.F.T., E.K., D.M.S., and B.J.B. have filed a patent application covering aspects of this work. B.J.B. is listed as an inventor on patent applications related to the SABER technology related to this work.

Over the past several years, the emergence of novel SARS-CoV-2 variants has led to multiple waves of increased COVID-19 incidence. When the Omicron variant emerged, there was considerable concern about its potential impact in the winter of 2021-2022 due to its increased fitness. However, there was also considerable uncertainty regarding its likely impact due to questions about its relative transmissibility, severity, and degree of immune escape. We sought to evaluate the ability of an agent-based model to forecast incidence in the context of this emerging pathogen variant. To project COVID-19 cases and deaths in Indiana, we calibrated our model to COVID-19 hospitalizations, deaths, and test-positivity rates through November 2021, and then projected COVID-19 incidence through April 2022 under four different scenarios that covered the plausible ranges of Omicron's severity, transmissibility, and degree of immune escape. Our initial projections from December 2021 through March 2022 indicated that under a pessimistic scenario with high disease severity, the peak in weekly COVID-19 deaths in Indiana would be larger than the previous peak in December 2020. However, retrospective analyses indicate that Omicron's severity was closer to the optimistic scenario, and even though cases and hospitalizations reached a new peak, fewer deaths occurred than during the previous peak. According to our results, Omicron's rapid spread was consistent with a combination of higher transmissibility and immune escape relative to earlier variants. Our updated projections starting in January 2022 accurately predicted that cases would peak in mid-January and decline rapidly over the next several months. The performance of our projections shows that following the emergence of a new pathogen variant, models can help quantify the potential range of outbreak magnitudes and trajectories. Agent-based models are particularly useful in these scenarios because they can efficiently track individual vaccination and infection histories with multiple variants with varying degrees of cross-protection., Competing Interests: Declaration of Competing Interest Authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens., (© 2024. The Author(s).)

Background: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval)., Methods and Findings: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths., Conclusions: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year., Competing Interests: JE is president of General Biodefense LLC, a private consulting group for public health informatics, and has interest in READE.ai, a medical artificial intelligence solutions company. MR reports stock ownership in Becton Dickinson & Co., which manufactures medical equipment used in COVID-19 testing, vaccination, and treatment. JL has served as an expert witness on cases where the likely length of the pandemic was of issue. The remaining authors declare no competing interests., (Copyright: © 2024 Jung 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: 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.)

Objectives: To evaluate the impact of implementing a stakeholder-informed social risk screening and social service referral system in a community hospital setting., Methods: We implemented a stakeholder-informed social care program at a community hospital in April 2022. The evaluation included patients aged 0 to 17 years admitted to the pediatric unit between April 2021 and March 2022 (1 year preimplementation) and between April 2022 and March 2023 (1 year postimplementation). For a random subset of 232 preimplementation and 218 postimplementation patients, we performed manual data extraction, documenting program process measures and preliminary effectiveness outcomes. We used χ square and Wilcoxon rank tests to compare outcomes between the preimplementation and postimplementation groups. Multivariable logistic regression was used to assess the preliminary effectiveness of the social care program in identifying social risks., Results: Screening rates were higher in the postimplementation group for nearly all social domains. Compared with preimplementation, the postimplementation group had higher rates of social risks identified (17.4% vs 7.8% [P < .01]: adjusted odds ratio 2.9 [95% confidence interval 1.5-5.5]) on multivariate testing. Social work consults were completed more frequently and earlier for the postimplementation group (13.8.% vs 5.6% [P < .01]) and median (19 hours vs 25 hours [P = .03]), respectively. Rates of communication of social risks in discharge summaries were higher in the postimplementation group (46.8% vs 8.2% [P < .001])., Conclusions: The implementation of a stakeholder-informed social care program within a community hospital setting led to the increased identification of social risks and social work consultations and improved timeliness of social work consultations and written communication of social risks in discharge summaries for primary care providers., (Copyright © 2024 by the American Academy of Pediatrics.)

Assessing the factors responsible for differences in outbreak severity for the same pathogen is a challenging task, since outbreak data are often incomplete and may vary in type across outbreaks (e.g., daily case counts, serology, cases per household). We propose that outbreaks described with varied data types can be directly compared by using those data to estimate a common set of epidemiological parameters. To demonstrate this for chikungunya virus (CHIKV), we developed a realistic model of CHIKV transmission, along with a Bayesian inference method that accommodates any type of outbreak data that can be simulated. The inference method makes use of the fact that all data types arise from the same transmission process, which is simulated by the model. We applied these tools to data from three real-world outbreaks of CHIKV in Italy, Cambodia, and Bangladesh to estimate nine model parameters. We found that these populations differed in several parameters, including pre-existing immunity and house-to-house differences in mosquito activity. These differences resulted in posterior predictions of local CHIKV transmission risk that varied nearly fourfold: 16% in Italy, 28% in Cambodia, and 62% in Bangladesh. Our inference method and model can be applied to improve understanding of the epidemiology of CHIKV and other pathogens for which outbreaks are described with varied data types., Competing Interests: Declaration of competing interest This study was funded by Bavarian Nordic A/S. STS and SMG are employees of Bavarian Nordic A/S. TAP receives consulting fees and research support from Bavarian Nordic A/S., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Dengue virus (DENV) transmission from humans to mosquitoes is a poorly documented, but critical component of DENV epidemiology. Magnitude of viremia is the primary determinant of successful human-to-mosquito DENV transmission. People with the same level of viremia, however, can vary in their infectiousness to mosquitoes as a function of other factors that remain to be elucidated. Here, we report on a field-based study in the city of Iquitos, Peru, where we conducted direct mosquito feedings on people naturally infected with DENV and that experienced mild illness. We also enrolled people naturally infected with Zika virus (ZIKV) after the introduction of ZIKV in Iquitos during the study period. Of the 54 study participants involved in direct mosquito feedings, 43 were infected with DENV-2, two with DENV-3, and nine with ZIKV. Our analysis excluded participants whose viremia was detectable at enrollment but undetectable at the time of mosquito feeding, which was the case for all participants with DENV-3 and ZIKV infections. We analyzed the probability of onward transmission during 50 feeding events involving 27 participants infected with DENV-2 based on the presence of infectious virus in mosquito saliva 7-16 days post blood meal. Transmission probability was positively associated with the level of viremia and duration of extrinsic incubation in the mosquito. In addition, transmission probability was influenced by the day of illness in a non-monotonic fashion; i.e., transmission probability increased until 2 days after symptom onset and decreased thereafter. We conclude that mildly ill DENV-infected humans with similar levels of viremia during the first two days after symptom onset will be most infectious to mosquitoes on the second day of their illness. Quantifying variation within and between people in their contribution to DENV transmission is essential to better understand the biological determinants of human infectiousness, parametrize epidemiological models, and improve disease surveillance and prevention strategies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

New vector-control technologies to fight mosquito-borne diseases are urgently needed, the adoption of which depends on efficacy estimates from large-scale cluster-randomised trials (CRTs). The release of Wolbachia -infected mosquitoes is one promising strategy to curb dengue virus (DENV) transmission, and a recent CRT reported impressive reductions in dengue incidence following the release of these mosquitoes. Such trials can be affected by multiple sources of bias, however. We used mathematical models of DENV transmission during a CRT of Wolbachia -infected mosquitoes to explore three such biases: human movement, mosquito movement and coupled transmission dynamics between trial arms. We show that failure to account for each of these biases would lead to underestimated efficacy, and that the majority of this underestimation is due to a heretofore unrecognised bias caused by transmission coupling. Taken together, our findings suggest that Wolbachia -infected mosquitoes could be even more promising than the recent CRT suggested. By emphasising the importance of accounting for transmission coupling between arms, which requires a mathematical model, we highlight the key role that models can play in interpreting and extrapolating the results from trials of vector control interventions., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023. Published by Elsevier B.V.)

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

The mosquito Aedes aegypti is the vector of a number of medically-important viruses, including dengue virus, yellow fever virus, chikungunya virus, and Zika virus, and as such vector control is a key approach to managing the diseases they cause. Understanding the impact of vector control on these diseases is aided by first understanding its impact on Ae. aegypti population dynamics. A number of detail-rich models have been developed to couple the dynamics of the immature and adult stages of Ae. aegypti. The numerous assumptions of these models enable them to realistically characterize impacts of mosquito control, but they also constrain the ability of such models to reproduce empirical patterns that do not conform to the models' behavior. In contrast, statistical models afford sufficient flexibility to extract nuanced signals from noisy data, yet they have limited ability to make predictions about impacts of mosquito control on disease caused by pathogens that the mosquitoes transmit without extensive data on mosquitoes and disease. Here, we demonstrate how the differing strengths of mechanistic realism and statistical flexibility can be fused into a single model. Our analysis utilizes data from 176,352 household-level Ae. aegypti aspirator collections conducted during 1999-2011 in Iquitos, Peru. The key step in our approach is to calibrate a single parameter of the model to spatio-temporal abundance patterns predicted by a generalized additive model (GAM). In effect, this calibrated parameter absorbs residual variation in the abundance time-series not captured by other features of the mechanistic model. We then used this calibrated parameter and the literature-derived parameters in the agent-based model to explore Ae. aegypti population dynamics and the impact of insecticide spraying to kill adult mosquitoes. The baseline abundance predicted by the agent-based model closely matched that predicted by the GAM. Following spraying, the agent-based model predicted that mosquito abundance rebounds within about two months, commensurate with recent experimental data from Iquitos. Our approach was able to accurately reproduce abundance patterns in Iquitos and produce a realistic response to adulticide spraying, while retaining sufficient flexibility to be applied across a range of settings., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Current knowledge of dengue virus (DENV) transmission provides only a partial understanding of a complex and dynamic system yielding a public health track record that has more failures than successes. An important part of the problem is that the foundation for contemporary interventions includes a series of longstanding, but untested, assumptions based on a relatively small portion of the human population; i.e., people who are convenient to study because they manifest clinically apparent disease. Approaching dengue from the perspective of people with overt illness has produced an extensive body of useful literature. It has not, however, fully embraced heterogeneities in virus transmission dynamics that are increasingly recognized as key information still missing in the struggle to control the most important insect-transmitted viral infection of humans. Only in the last 20 years have there been significant efforts to carry out comprehensive longitudinal dengue studies. This manuscript provides the rationale and comprehensive, integrated description of the methodology for a five-year longitudinal cohort study based in the tropical city of Iquitos, in the heart of the Peruvian Amazon. Primary data collection for this study was completed in 2019. Although some manuscripts have been published to date, our principal objective here is to support subsequent publications by describing in detail the structure, methodology, and significance of a specific research program. Our project was designed to study people across the entire continuum of disease, with the ultimate goal of quantifying heterogeneities in human variables that affect DENV transmission dynamics and prevention. Because our study design is applicable to other Aedes transmitted viruses, we used it to gain insights into Zika virus (ZIKV) transmission when during the project period ZIKV was introduced and circulated in Iquitos. Our prospective contact cluster investigation design was initiated by detecttion of a person with a symptomatic DENV infection and then followed that person's immediate contacts. This allowed us to monitor individuals at high risk of DENV infection, including people with clinically inapparent and mild infections that are otherwise difficult to detect. We aimed to fill knowledge gaps by defining the contribution to DENV transmission dynamics of (1) the understudied majority of DENV-infected people with inapparent and mild infections and (2) epidemiological, entomological, and socio-behavioral sources of heterogeneity. By accounting for factors underlying variation in each person's contribution to transmission we sought to better determine the type and extent of effort needed to better prevent virus transmission and disease., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

The five major Plasmodium spp. that cause human malaria appear similar under light microscopy, which raises the possibility that misdiagnosis could routinely occur in clinical settings. Assessing the extent of misdiagnosis is of particular importance for monitoring P. knowlesi, which cocirculates with the other Plasmodium spp. We performed a systematic review and meta-analysis of studies comparing the performance of microscopy and polymerase chain reaction (PCR) for diagnosing malaria in settings with co-circulation of the five Plasmodium spp. We assessed the extent to which co-circulation of Plasmodium parasites affects diagnostic outcomes. We fit a Bayesian hierarchical latent class model to estimate variation in microscopy sensitivity and specificity measured against PCR as the gold standard. Mean sensitivity of microscopy was low, yet highly variable across Plasmodium spp., ranging from 65.7% (95% confidence interval: 48.1-80.3%) for P. falciparum to 0.525% (95% confidence interval 0.0210-3.11%) for P. ovale. Observed PCR prevalence was positively correlated with estimated microscopic sensitivity and negatively correlated with estimated microscopic specificity, though the strength of the associations varied by species. Our analysis suggests that cocirculation of Plasmodium spp. undermines the accuracy of microscopy. Sensitivity was considerably lower for P. knowlesi, P. malariae, and P. ovale. The negative association between specificity and prevalence imply that less frequently encountered species may be misdiagnosed as more frequently encountered species. Together, these results suggest that the burden of P. knowlesi, P. malariae, and P. ovale may be underappreciated in a clinical setting.

Spatial repellent (SR) products are envisioned to complement existing vector control methods through the continual release of volatile active ingredients (AI) providing: (i) protection against day-time and early-evening biting; (ii) protection in enclosed/semi-enclosed and peri-domestic spaces; (iii) various formulations to fit context-specific applications; and (iv) increased coverage over traditional control methods. SR product AIs also have demonstrated effect against insecticide-resistant vectors linked to malaria and Aedes- borne virus (ABV) transmission. Over the past two decades, key stakeholders, including World Health Organization (WHO) representatives, have met to discuss the role of SRs in reducing arthropod-borne diseases based on existing evidence. A key focus has been to establish a critical development path for SRs, including scientific, regulatory and social parameters that would constitute an outline for a SR target product profile, i.e. optimum product characteristics. The principal gap is the lack of epidemiological data demonstrating SR public health impact across a range of different ecological and epidemiological settings, to inform a WHO policy recommendation. Here we describe in brief trials that are designed to fulfill evidence needs for WHO assessment and initial projections of SR cost-effectiveness against malaria and dengue., Competing Interests: 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., (© 2022 The Authors.)

Like other congregate living settings, military basic training has been subject to outbreaks of COVID-19. We sought to identify improved strategies for preventing outbreaks in this setting using an agent-based model of a hypothetical cohort of trainees on a U.S. Army post. Our analysis revealed unique aspects of basic training that require customized approaches to outbreak prevention, which draws attention to the possibility that customized approaches may be necessary in other settings, too. In particular, we showed that introductions by trainers and support staff may be a major vulnerability, given that those individuals remain at risk of community exposure throughout the training period. We also found that increased testing of trainees upon arrival could actually increase the risk of outbreaks, given the potential for false-positive test results to lead to susceptible individuals becoming infected in group isolation and seeding outbreaks in training units upon release. Until an effective transmission-blocking vaccine is adopted at high coverage by individuals involved with basic training, need will persist for non-pharmaceutical interventions to prevent outbreaks in military basic training. Ongoing uncertainties about virus variants and breakthrough infections necessitate continued vigilance in this setting, even as vaccination coverage increases., Competing Interests: The authors have declared that no competing interests exist.

When related segmented RNA viruses co-infect a single cell, viral reassortment can occur, potentially leading to new strains with pandemic potential. One virus capable of reassortment is bluetongue virus (BTV), which causes substantial health impacts in ruminants and is transmitted via Culicoides midges. Because midges can become co-infected by feeding on multiple different host species and remain infected for their entire life span, there is a high potential for reassortment to occur. Once a midge is co-infected, additional barriers must be crossed for a reassortant virus to emerge, such as cellular co-infection and dissemination of reassortant viruses to the salivary glands. We developed three mathematical models of within-midge BTV dynamics of increasing complexity, allowing us to explore the conditions leading to the emergence of reassortant viruses. In confronting the simplest model with published data, we estimate that the average life span of a bluetongue virion in the midge midgut is about 6 h, a key determinant of establishing a successful infection. Examination of the full model, which permits cellular co-infection and reassortment, shows that small differences in fitness of the two infecting strains can have a large impact on the frequency with which reassortant virions are observed. This is consistent with experimental co-infection studies with BTV strains with different relative fitnesses that did not produce reassortant progeny. Our models also highlight several gaps in existing data that would allow us to elucidate these dynamics in more detail, in particular the times it takes the virus to disseminate to different tissues, and measurements of viral load and reassortant frequency at different temperatures., (© The Author(s) 2022. Published by Oxford University Press.)

Background: Despite large outbreaks in humans seeming improbable for a number of zoonotic pathogens, several pose a concern due to their epidemiological characteristics and evolutionary potential. To enable effective responses to these pathogens in the event that they undergo future emergence, the Coalition for Epidemic Preparedness Innovations is advancing the development of vaccines for several pathogens prioritized by the World Health Organization. A major challenge in this pursuit is anticipating demand for a vaccine stockpile to support outbreak response., Methods: We developed a modeling framework for outbreak response for emerging zoonoses under three reactive vaccination strategies to assess sustainable vaccine manufacturing needs, vaccine stockpile requirements, and the potential impact of the outbreak response. This framework incorporates geographically variable zoonotic spillover rates, human-to-human transmission, and the implementation of reactive vaccination campaigns in response to disease outbreaks. As proof of concept, we applied the framework to four priority pathogens: Lassa virus, Nipah virus, MERS coronavirus, and Rift Valley virus., Results: Annual vaccine regimen requirements for a population-wide strategy ranged from > 670,000 (95% prediction interval 0-3,630,000) regimens for Lassa virus to 1,190,000 (95% PrI 0-8,480,000) regimens for Rift Valley fever virus, while the regimens required for ring vaccination or targeting healthcare workers (HCWs) were several orders of magnitude lower (between 1/25 and 1/700) than those required by a population-wide strategy. For each pathogen and vaccination strategy, reactive vaccination typically prevented fewer than 10% of cases, because of their presently low R 0 values. Targeting HCWs had a higher per-regimen impact than population-wide vaccination., Conclusions: Our framework provides a flexible methodology for estimating vaccine stockpile needs and the geographic distribution of demand under a range of outbreak response scenarios. Uncertainties in our model estimates highlight several knowledge gaps that need to be addressed to target vulnerable populations more accurately. These include surveillance gaps that mask the true geographic distribution of each pathogen, details of key routes of spillover from animal reservoirs to humans, and the role of human-to-human transmission outside of healthcare settings. In addition, our estimates are based on the current epidemiology of each pathogen, but pathogen evolution could alter vaccine stockpile requirements., (© 2022. The Author(s).)

Accurate tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been critical in efforts to control its spread. The accuracy of tests for SARS-CoV-2 has been assessed numerous times, usually in reference to a gold standard diagnosis. One major disadvantage of that approach is the possibility of error due to inaccuracy of the gold standard, which is especially problematic for evaluating testing in a real-world surveillance context. We used an alternative approach known as Bayesian latent class modeling (BLCM), which circumvents the need to designate a gold standard by simultaneously estimating the accuracy of multiple tests. We applied this technique to a collection of 1,716 tests of three types applied to 853 individuals on a university campus during a 1-week period in October 2020. We found that reverse transcriptase PCR (RT-PCR) testing of saliva samples performed at a campus facility had higher sensitivity (median, 92.3%; 95% credible interval [CrI], 73.2 to 99.6%) than RT-PCR testing of nasal samples performed at a commercial facility (median, 85.9%; 95% CrI, 54.7 to 99.4%). The reverse was true for specificity, although the specificity of saliva testing was still very high (median, 99.3%; 95% CrI, 98.3 to 99.9%). An antigen test was less sensitive and specific than both of the RT-PCR tests, although the sample sizes with this test were small and the statistical uncertainty was high. These results suggest that RT-PCR testing of saliva samples at a campus facility can be an effective basis for surveillance screening to prevent SARS-CoV-2 transmission in a university setting. IMPORTANCE Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been vitally important during the COVID-19 pandemic. There are a variety of methods for testing for this virus, and it is important to understand their accuracy in choosing which one might be best suited for a given application. To estimate the accuracy of three different testing methods, we used a data set collected at a university that involved testing the same samples with multiple tests. Unlike most other estimates of test accuracy, we did not assume that one test was perfect but instead allowed for some degree of inaccuracy in all testing methods. We found that molecular tests performed on saliva samples at a university facility were similarly accurate as molecular tests performed on nasal samples at a commercial facility. An antigen test appeared somewhat less accurate than the molecular tests, but there was high uncertainty about that.

Background: Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated., Methods: The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated., Results: The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, R c . Applying this approach to data from Eswatini indicated that inferences of R c and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data., Conclusions: These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution., (© 2022. The Author(s).)

Bluetongue virus (BTV) is an arthropod-borne, segmented double-stranded RNA virus that can cause severe disease in both wild and domestic ruminants. BTV evolves via several key mechanisms, including the accumulation of mutations over time and the reassortment of genome segments.Additionally, BTV must maintain fitness in two disparate hosts, the insect vector and the ruminant. The specific features of viral adaptation in each host that permit host-switching are poorly characterized. Limited field studies and experimental work have alluded to the presence of these phenomena at work, but our understanding of the factors that drive or constrain BTV's genetic diversification remains incomplete. Current research leveraging novel approaches and whole genome sequencing applications promises to improve our understanding of BTV's evolution, ultimately contributing to the development of better predictive models and management strategies to reduce future impacts of bluetongue epizootics.

Competing Interests: We declare no competing interests.

In the United States, schools closed in March 2020 due to COVID-19 and began reopening in August 2020, despite continuing transmission of SARS-CoV-2. In states where in-person instruction resumed at that time, two major unknowns were the capacity at which schools would operate, which depended on the proportion of families opting for remote instruction, and adherence to face-mask requirements in schools, which depended on cooperation from students and enforcement by schools. To determine the impact of these conditions on the statewide burden of COVID-19 in Indiana, we used an agent-based model calibrated to and validated against multiple data types. Using this model, we quantified the burden of COVID-19 on K-12 students, teachers, their families, and the general population under alternative scenarios spanning three levels of school operating capacity (50 %, 75 %, and 100 %) and three levels of face-mask adherence in schools (50 %, 75 %, and 100 %). Under a scenario in which schools operated remotely, we projected 45,579 (95 % CrI: 14,109-132,546) infections and 790 (95 % CrI: 176-1680) deaths statewide between August 24 and December 31. Reopening at 100 % capacity with 50 % face-mask adherence in schools resulted in a proportional increase of 42.9 (95 % CrI: 41.3-44.3) and 9.2 (95 % CrI: 8.9-9.5) times that number of infections and deaths, respectively. In contrast, our results showed that at 50 % capacity with 100 % face-mask adherence, the number of infections and deaths were 22 % (95 % CrI: 16 %-28 %) and 11 % (95 % CrI: 5 %-18 %) higher than the scenario in which schools operated remotely. Within this range of possibilities, we found that high levels of school operating capacity (80-95 %) and intermediate levels of face-mask adherence (40-70 %) resulted in model behavior most consistent with observed data. Together, these results underscore the importance of precautions taken in schools for the benefit of their communities., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Estimates of disease burden are important for setting public health priorities. These estimates involve numerous modeling assumptions, whose uncertainties are not always well described. We developed a framework for estimating the burden of yellow fever in Africa and evaluated its sensitivity to modeling assumptions that are often overlooked. We found that alternative interpretations of serological data resulted in a nearly 20-fold difference in burden estimates (range of central estimates, 8.4 × 10 4 to 1.5 × 10 6 deaths in 2021–2030). Uncertainty about the vaccination status of serological study participants was the primary driver of this uncertainty. Even so, statistical uncertainty was even greater than uncertainty due to modeling assumptions, accounting for a total of 87% of variance in burden estimates. Combined with estimates that most infections go unreported (range of 95% credible intervals, 99.65 to 99.99%), our results suggest that yellow fever’s burden will remain highly uncertain without major improvements in surveillance.

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases., (© 2021. The Author(s).)

Background: The COVID-19 pandemic has induced unprecedented reductions in human mobility and social contacts throughout the world. Because dengue virus (DENV) transmission is strongly driven by human mobility, behavioral changes associated with the pandemic have been hypothesized to impact dengue incidence. By discouraging human contact, COVID-19 control measures have also disrupted dengue vector control interventions, the most effective of which require entry into homes. We sought to investigate how and why dengue incidence could differ under a lockdown scenario with a proportion of the population sheltered at home., Methodology & Principal Findings: We used an agent-based model with a realistic treatment of human mobility and vector control. We found that a lockdown in which 70% of the population sheltered at home and which occurred in a season when a new serotype invaded could lead to a small average increase in cumulative DENV infections of up to 10%, depending on the time of year lockdown occurred. Lockdown had a more pronounced effect on the spatial distribution of DENV infections, with higher incidence under lockdown in regions with higher mosquito abundance. Transmission was also more focused in homes following lockdown. The proportion of people infected in their own home rose from 54% under normal conditions to 66% under lockdown, and the household secondary attack rate rose from 0.109 to 0.128, a 17% increase. When we considered that lockdown measures could disrupt regular, city-wide vector control campaigns, the increase in incidence was more pronounced than with lockdown alone, especially if lockdown occurred at the optimal time for vector control., Conclusions & Significance: Our results indicate that an unintended outcome of lockdown measures may be to adversely alter the epidemiology of dengue. This observation has important implications for an improved understanding of dengue epidemiology and effective application of dengue vector control. When coordinating public health responses during a syndemic, it is important to monitor multiple infections and understand that an intervention against one disease may exacerbate another., Competing Interests: The authors have declared that no competing interests exist.

An effective and widely used vaccine could reduce the burden of dengue virus (DENV) around the world. DENV is endemic in Puerto Rico, where the dengue vaccine CYD-TDV is currently under consideration as a control measure. CYD-TDV has demonstrated efficacy in clinical trials in vaccinees who had prior dengue virus infection. However, in vaccinees who had no prior dengue virus infection, the vaccine had a modestly elevated risk of hospitalization and severe disease. The WHO therefore recommended a strategy of pre-vaccination screening and vaccination of seropositive persons. To estimate the cost-effectiveness and benefits of this intervention (i.e., screening and vaccination of seropositive persons) in Puerto Rico, we simulated 10 years of the intervention in 9-year-olds using an agent-based model. Across the entire population, we found that 5.5% (4.6%-6.3%) of dengue hospitalizations could be averted. However, we also found that 0.057 (0.045-0.073) additional hospitalizations could occur for every 1,000 people in Puerto Rico due to DENV-naïve children who were vaccinated following a false-positive test results for prior exposure. The ratio of the averted hospitalizations among all vaccinees to additional hospitalizations among DENV-naïve vaccinees was estimated to be 19 (13-24). At a base case cost of vaccination of 382 USD, we found an incremental cost-effectiveness ratio of 122,000 USD per QALY gained. Our estimates can provide information for considerations to introduce the CYD-TDV vaccine in Puerto Rico., Competing Interests: The authors have declared that no competing interests exist.

This article presents a versatile neurostimulation platform featuring a fully implantable multi-channel neural stimulator for chronic experimental studies with freely moving large animal models involving peripheral nerves. The implant is hermetically sealed in a ceramic enclosure and encapsulated in medical grade silicone rubber, and then underwent active tests at accelerated aging conditions at 100°C for 15 consecutive days. The stimulator microelectronics are implemented in a 0.6-μm CMOS technology, with a crosstalk reduction scheme to minimize cross-channel interference, and high-speed power and data telemetry for battery-less operation. A wearable transmitter equipped with a Bluetooth Low Energy radio link, and a custom graphical user interface provide real-time, remotely controlled stimulation. Three parallel stimulators provide independent stimulation on three channels, where each stimulator supports six stimulating sites and two return sites through multiplexing, hence the implant can facilitate stimulation at up to 36 different electrode pairs. The design of the electronics, method of hermetic packaging and electrical performance as well as in vitro testing with electrodes in saline are presented., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jiang, Liu, Lancashire, Perkins, Schormans, Vanhoestenberghe, Donaldson and Demosthenous.)

Background: Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility., Methodology and Principal Findings: Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low "health-related quality of well-being" during illness (Fisher's Exact, p = 0.01)., Conclusions/significance: Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual's exposure to virus or a presymptomatic/clinically inapparent individual's contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission., Competing Interests: The authors have declared that no competing interests exist.

Background: Vaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae , and yellow fever over the years 2000-2030 across 112 countries., Methods: Twenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios., Results: We estimate 97 (95%CrI[80, 120]) million deaths would be averted due to vaccination activities over 2000-2030, with 50 (95%CrI[41, 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030, we estimate 52 (95%CrI[41, 69]) million more deaths would occur over their lifetimes without vaccination against these diseases., Conclusions: This study represents the largest assessment of vaccine impact before COVID-19-related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future., Funding: VIMC is jointly funded by Gavi, the Vaccine Alliance, and the Bill and Melinda Gates Foundation (BMGF) (BMGF grant number: OPP1157270 / INV-009125). Funding from Gavi is channelled via VIMC to the Consortium's modelling groups (VIMC-funded institutions represented in this paper: Imperial College London, London School of Hygiene and Tropical Medicine, Oxford University Clinical Research Unit, Public Health England, Johns Hopkins University, The Pennsylvania State University, Center for Disease Analysis Foundation, Kaiser Permanente Washington, University of Cambridge, University of Notre Dame, Harvard University, Conservatoire National des Arts et Métiers, Emory University, National University of Singapore). Funding from BMGF was used for salaries of the Consortium secretariat (authors represented here: TBH, MJ, XL, SE-L, JT, KW, NMF, KAMG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors). We also acknowledge funding from the UK Medical Research Council and Department for International Development, which supported aspects of VIMC's work (MRC grant number: MR/R015600/1).JHH acknowledges funding from National Science Foundation Graduate Research Fellowship; Richard and Peggy Notebaert Premier Fellowship from the University of Notre Dame. BAL acknowledges funding from NIH/NIGMS (grant number R01 GM124280) and NIH/NIAID (grant number R01 AI112970). The Lives Saved Tool (LiST) receives funding support from the Bill and Melinda Gates Foundation.This paper was compiled by all coauthors, including two coauthors from Gavi. Other funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication., Competing Interests: JT, SE, XL, KA, EC, HC, AC, Md, KE, MF, IG, TH, WH, DH, JH, KJ, AK, PK, AK, JL, XL, TM, CM, SM, SN, TP, AP, DR, SR, CS, SS, YT, HT, QT, ST, EV, NW, AW, KW, NF, KG No competing interests declared, MJ MLJ has received research funding from Sanofi Pasteur unrelated to the present work, MJ Reviewing editor, eLife, BL BAL reports grants and personal fees from Takeda Pharmaceuticals, personal fees from World Health Organization, outside the submitted work, TP TAP receives support from Emergent Biosolutions for work unrelated to his contribution to this study, HR HR is an employee of Center for Disease Analysis Foundation which has received grants from Gilead Sciences, AbbVie, Zeshan Foundation and EndHep2030 fund for projects unrelated to this work; HBV epidemiology data was funded by a grant from John Martin Foundation (Grant number 24), CT CLT received a consulting payment from GSK in 2018 (outside the submitted work), (© 2021, Toor et al.)

Background: Childhood immunisation services have been disrupted by the COVID-19 pandemic. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic., Methods: We used two to three models per infection to estimate the health impact of 50% reduced routine vaccination coverage in 2020 and delay of campaign vaccination from 2020 to 2021 for measles vaccination in Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan, for meningococcal A vaccination in Burkina Faso, Chad, Niger, and Nigeria, and for yellow fever vaccination in the Democratic Republic of Congo, Ghana, and Nigeria. Our counterfactual comparative scenario was sustaining immunisation services at coverage projections made prior to COVID-19 (i.e. without any disruption)., Results: Reduced routine vaccination coverage in 2020 without catch-up vaccination may lead to an increase in measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns in Ethiopia and Nigeria by a year may significantly increase the risk of measles outbreaks (both countries did complete their supplementary immunisation activities (SIAs) planned for 2020). For yellow fever vaccination, delay in campaigns leads to a potential disease burden rise of >1 death per 100,000 people per year until the campaigns are implemented. For meningococcal A vaccination, short-term disruptions in 2020 are unlikely to have a significant impact due to the persistence of direct and indirect benefits from past introductory campaigns of the 1- to 29-year-old population, bolstered by inclusion of the vaccine into the routine immunisation schedule accompanied by further catch-up campaigns., Conclusions: The impact of COVID-19-related disruption to vaccination programs varies between infections and countries. Planning and implementation of campaigns should consider country and infection-specific epidemiological factors and local immunity gaps worsened by the COVID-19 pandemic when prioritising vaccines and strategies for catch-up vaccination., Funding: Bill and Melinda Gates Foundation and Gavi, the Vaccine Alliance., Competing Interests: KG, KA, JH, AK, KW, XL, SE, MF, KM, TP No competing interests declared, NT NT is an employee of the Institute for Disease Modeling at the Bill & Melinda Gates Foundation, which funded the research. MJ KM is an employee of the Institute for Disease Modeling at the Bill & Melinda Gates Foundation, which funded the research. CT CT declares a consultancy fee from GSK in 2018 (unrelated to the submitted work). MJ Reviewing editor, eLife, (© 2021, Gaythorpe et al.)

During the past 100 years, Rift Valley fever virus (RVFV), a mosquito-borne virus, has caused potentially lethal disease in livestock, and has been associated with significant economic losses and trade bans. Spillover to humans occurs and can be fatal. Here, we combined data on RVF disease in humans (22 countries) and animals (37 countries) from 1931 to 2020 with seroprevalence studies from 1950 to 2020 (n = 228) from publicly available databases and publications to draw a more complete picture of the past and current RVFV epidemiology. RVFV has spread from its original locus in Kenya throughout Africa and into the Arabian Peninsula. Throughout the study period seroprevalence increased in both humans and animals, suggesting potentially increased RVFV exposure. In 24 countries, animals or humans tested positive for RVFV antibodies even though outbreaks had never been reported there, suggesting RVFV transmission may well go unnoticed. Among ruminants, sheep were the most likely to be exposed during RVF outbreaks, but not during periods of cryptic spread. We discuss critical data gaps and highlight the need for detailed study descriptions, and long-term studies using a one health approach to further convert the patchwork of data to the tale of RFV epidemiology.

After the Zika virus (ZIKV) epidemic in the Americas in 2016, both Zika and dengue incidence declined to record lows in many countries in 2017-2018, but in 2019 dengue resurged in Brazil, causing ~2.1 million cases. In this study we use epidemiological, climatological and genomic data to investigate dengue dynamics in recent years in Brazil. First, we estimate dengue virus force of infection (FOI) and model mosquito-borne transmission suitability since the early 2000s. Our estimates reveal that DENV transmission was low in 2017-2018, despite conditions being suitable for viral spread. Our study also shows a marked decline in dengue susceptibility between 2002 and 2019, which could explain the synchronous decline of dengue in the country, partially as a result of protective immunity from prior ZIKV and/or DENV infections. Furthermore, we performed phylogeographic analyses using 69 newly sequenced genomes of dengue virus serotype 1 and 2 from Brazil, and found that the outbreaks in 2018-2019 were caused by local DENV lineages that persisted for 5-10 years, circulating cryptically before and after the Zika epidemic. We hypothesize that DENV lineages may circulate at low transmission levels for many years, until local conditions are suitable for higher transmission, when they cause major outbreaks.

During the 2015-2017 Zika epidemic, dengue and chikungunya-two other viral diseases with the same vector as Zika-were also in circulation. Clinical presentation of these diseases can vary from person to person in terms of symptoms and severity, making it difficult to differentially diagnose them. Under these circumstances, it is possible that numerous cases of Zika could have been misdiagnosed as dengue or chikungunya, or vice versa. Given the importance of surveillance data for informing epidemiological analyses, our aim was to quantify the potential extent of misdiagnosis during this epidemic. Using basic principles of probability and empirical estimates of diagnostic sensitivity and specificity, we generated revised estimates of reported cases of Zika that accounted for the accuracy of diagnoses made on the basis of clinical presentation with or without laboratory confirmation. Applying this method to weekly reported case data from 43 countries throughout Latin America and the Caribbean, we estimated that 944,700 (95% CrI: 884,900-996,400) Zika cases occurred when assuming all confirmed cases were diagnosed using molecular methods versus 608,400 (95% CrI: 442,000-821,800) Zika cases that occurred when assuming all confirmed cases were diagnosed using serological methods. Our results imply that misdiagnosis was more common in countries with proportionally higher reported cases of dengue and chikungunya, such as Brazil. Given that Zika, dengue, and chikungunya appear likely to co-circulate in the Americas and elsewhere for years to come, our methodology has the potential to enhance the interpretation of passive surveillance data for these diseases going forward. Likewise, our methodology could also be used to help resolve transmission dynamics of other co-circulating diseases with similarities in symptomatology and potential for misdiagnosis., Competing Interests: The authors have declared no competing interests exist.

Measles incidence in the United States has grown dramatically, as vaccination rates are declining and transmission internationally is on the rise. Because imported cases are necessary drivers of outbreaks in non-endemic settings, predicting measles outbreaks in the US depends on predicting imported cases. To assess the predictability of imported measles cases, we performed a regression of imported measles cases in the US against an inflow variable that combines air travel data with international measles surveillance data. To understand the contribution of each data type to these predictions, we repeated the regression analysis with alternative versions of the inflow variable that replaced each data type with averaged values and with versions of the inflow variable that used modeled inputs. We assessed the performance of these regression models using correlation, coverage probability, and area under the curve statistics, including with resampling and cross-validation. Our regression model had good predictive ability with respect to the presence or absence of imported cases in a given state in a given year (area under the curve of the receiver operating characteristic curve (AUC) = 0.78) and the magnitude of imported cases (Pearson correlation = 0.84). By comparing alternative versions of the inflow variable averaging over different inputs, we found that both air travel data and international surveillance data contribute to the model's ability to predict numbers of imported cases and individually contribute to its ability to predict the presence or absence of imported cases. Predicted sources of imported measles cases varied considerably across years and US states, depending on which countries had high measles activity in a given year. Our results emphasize the importance of the relationship between global connectedness and the spread of measles. This study provides a framework for predicting and understanding imported case dynamics that could inform future studies and outbreak prevention efforts.

Heterogeneous exposure to mosquitoes determines an individual's contribution to vector-borne pathogen transmission. Particularly for dengue virus (DENV), there is a major difficulty in quantifying human-vector contacts due to the unknown coupled effect of key heterogeneities. To test the hypothesis that the reduction of human out-of-home mobility due to dengue illness will significantly influence population-level dynamics and the structure of DENV transmission chains, we extended an existing modeling framework to include social structure, disease-driven mobility reductions, and heterogeneous transmissibility from different infectious groups. Compared to a baseline model, naïve to human pre-symptomatic infectiousness and disease-driven mobility changes, a model including both parameters predicted an increase of 37% in the probability of a DENV outbreak occurring; a model including mobility change alone predicted a 15.5% increase compared to the baseline model. At the individual level, models including mobility change led to a reduction of the importance of out-of-home onward transmission (R, the fraction of secondary cases predicted to be generated by an individual) by symptomatic individuals (up to -62%) at the expense of an increase in the relevance of their home (up to +40%). An individual's positive contribution to R could be predicted by a GAM including a non-linear interaction between an individual's biting suitability and the number of mosquitoes in their home (>10 mosquitoes and 0.6 individual attractiveness significantly increased R). We conclude that the complex fabric of social relationships and differential behavioral response to dengue illness cause the fraction of symptomatic DENV infections to concentrate transmission in specific locations, whereas asymptomatic carriers (including individuals in their pre-symptomatic period) move the virus throughout the landscape. Our findings point to the difficulty of focusing vector control interventions reactively on the home of symptomatic individuals, as this approach will fail to contain virus propagation by visitors to their house and asymptomatic carriers., Competing Interests: The authors have declared that no competing interests exist.

The CYD-TDV vaccine was recently developed to combat dengue, a mosquito-borne viral disease that afflicts millions of people each year throughout the tropical and subtropical world. Its rollout has been complicated by recent findings that vaccinees with no prior exposure to dengue virus (DENV) experience an elevated risk of severe disease in response to their first DENV infection subsequent to vaccination. As a result of these findings, guidelines for use of CYD-TDV now require serological screening prior to vaccination to establish that an individual does not fall into this high-risk category. These complications mean that the public health impact of CYD-TDV vaccination is expected to be higher in areas with higher transmission. One important practical difficulty with tailoring vaccination policy to local transmission contexts is that DENV transmission is spatially heterogeneous, even at the scale of neighborhoods or blocks within a city. This raises the question of whether models based on data that average over spatial heterogeneity in transmission could fail to capture important aspects of CYD-TDV impact in spatially heterogeneous populations. We explored this question with a deterministic model of DENV transmission and CYD-TDV vaccination in a population comprised of two communities with differing transmission intensities. Compared to the full model, a version of the model based on the average of the two communities failed to capture benefits of targeting the intervention to the high-transmission community, which resulted in greater impact in both communities than we observed under even coverage. In addition, the model based on the average of the two communities substantially overestimated impact among vaccinated individuals in the low-transmission community. In the event that the specificity of serological screening is not high, this result suggests that models that ignore spatial heterogeneity could overlook the potential for harm to this segment of the population., (© 2020 The Authors.)

Several hundred thousand Zika cases have been reported across the Americas since 2015. Incidence of infection was likely much higher, however, due to a high frequency of asymptomatic infection and other challenges that surveillance systems faced. Using a hierarchical Bayesian model with empirically-informed priors, we leveraged multiple types of Zika case data from 15 countries to estimate subnational reporting probabilities and infection attack rates (IARs). Zika IAR estimates ranged from 0.084 (95% CrI: 0.067-0.096) in Peru to 0.361 (95% CrI: 0.214-0.514) in Ecuador, with significant subnational variability in every country. Totaling infection estimates across these and 33 other countries and territories, our results suggest that 132.3 million (95% CrI: 111.3-170.2 million) people in the Americas had been infected by the end of 2018. These estimates represent the most extensive attempt to determine the size of the Zika epidemic in the Americas, offering a baseline for assessing the risk of future Zika epidemics in this region., Competing Interests: The authors have declared that no competing interests exist.

Spatial repellents (SRs) reduce human-mosquito contact by preventing mosquito entrance into human-occupied spaces and interfering with host-seeking and blood-feeding. A new model to synthesize experimental data on the effects of transfluthrin on Aedes aegypti explores how SR effects interact to impact the epidemiology of diseases vectored by these mosquitoes. Our results indicate that the greatest impact on force of infection is expected to derive from the chemical's lethal effect but delayed biting and the negative effect this may have on the mosquito population could elicit substantial impact in the absence of lethality. The relative contributions of these effects depend on coverage, chemical dose, and housing density. We also demonstrate that, through an increase in the number of potentially infectious mosquito bites, increased partial blood-feeding and reduced exiting may elicit adverse impacts, which could offset gains achieved by other effects. Our analysis demonstrates how small-scale experimental data can be leveraged to derive expectations of epidemiological impact of SRs deployed at larger scales., Competing Interests: The authors have declared that no competing interests exist.

By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during its initial invasion of the United States remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, we estimated that 108,689 (95% posterior predictive interval [95% PPI]: 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model's predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.98; 95% PPI: 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)