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Background: Marine recruits training at Parris Island experienced an unexpectedly high rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, despite preventive measures including a supervised, 2-week, pre-entry quarantine. We characterize SARS-CoV-2 transmission in this cohort., Methods: Between May and November 2020, we monitored 2,469 unvaccinated, mostly male, Marine recruits prospectively during basic training. If participants tested negative for SARS-CoV-2 by quantitative polymerase chain reaction (qPCR) at the end of quarantine, they were transferred to the training site in segregated companies and underwent biweekly testing for 6 weeks. We assessed the effects of coronavirus disease 2019 (COVID-19) prevention measures on other respiratory infections with passive surveillance data, performed phylogenetic analysis, and modeled transmission dynamics and testing regimens., Results: Preventive measures were associated with drastically lower rates of other respiratory illnesses. However, among the trainees, 1,107 (44.8%) tested SARS-CoV-2-positive, with either mild or no symptoms. Phylogenetic analysis of viral genomes from 580 participants revealed that all cases but one were linked to five independent introductions, each characterized by accumulation of mutations across and within companies, and similar viral isolates in individuals from the same company. Variation in company transmission rates (mean reproduction number R 0 ; 5.5 [95% confidence interval [CI], 5.0, 6.1]) could be accounted for by multiple initial cases within a company and superspreader events. Simulations indicate that frequent rapid-report testing with case isolation may minimize outbreaks., Conclusions: Transmission of wild-type SARS-CoV-2 among Marine recruits was approximately twice that seen in the community. Insights from SARS-CoV-2 outbreak dynamics and mutations spread in a remote, congregate setting may inform effective mitigation strategies., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact., Methods and Findings: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses., Conclusions: Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: JL is a paid statistical advisor for PLOS Medicine.

Objectives: Quantitative estimates of the impact of infectious disease outbreaks are required to develop measured policy responses. In many low- and middle-income countries, inadequate surveillance and incompleteness of death registration are important barriers., Design: Here, we characterize how large an impact on mortality would have to be for being detectable using the uniquely detailed mortality notification data from the city of Antananarivo, Madagascar, with application to a recent measles outbreak., Results: The weekly mortality rate of children during the 2018-2019 measles outbreak was 161% above the expected value at its peak, and the signal can be detected earlier in children than in the general population. This approach to detect anomalies from expected baseline mortality allows us to delineate the prevalence of COVID-19 at which excess mortality would be detectable with the existing death notification system in Antananarivo., Conclusions: Given current age-specific estimates of the COVID-19 fatality ratio and the age structure of the population in Antananarivo, we estimate that as few as 11 deaths per week in the 60-70 years age group (corresponding to an infection rate of approximately 1%) would detectably exceed the baseline. Data from 2020 will undergo necessary processing and quality control in the coming months. Our results provide a baseline for interpreting this information., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A minimalist model of ecohydrologic dynamics is coupled to the well-known susceptible-infected-recovered epidemiological model to explore hydro-climatic controls on infection dynamics and extreme outbreaks. The resulting HYSIR model reveals the existence of a noise-induced bifurcation producing oscillations in infection dynamics. Linearization of the governing equations allows for an analytic expression for the periodicity of infections in terms of both epidemiological (e.g. transmission and recovery rate) and hydrologic (i.e. soil moisture decay rate or memory) parameters. Numerical simulations of the full stochastic, nonlinear system show extreme outbreaks in response to particular combinations of hydro-climatic conditions, neither of which is extreme per se , rather than a single major climatic event. These combinations depend on the assumed functional relationship between the hydrologic variables and the transmission rate. Our results emphasize the importance of hydro-climatic history and system memory in evaluating the risk of severe outbreaks.

Rubella virus causes mild disease in children but for women in the early stages of pregnancy, it can cause spontaneous abortion, congenital rubella syndrome (CRS) and associated birth defects. Despite the availability of an effective vaccine, rubella virus continues to circulate endemically in several regions of the world. This is particularly true in East and Southeast (E/SE) Asia, where control efforts vary widely among countries that are well connected through travel and immigration. It is therefore important to understand how the regional persistence of rubella is affected both by dynamics occurring across countries and susceptibility within countries. Here, we use genetic and epidemiological data from countries in E/SE Asia to explore the phylogeography of rubella virus in this region. Our results underline that metapopulation dynamics are key for rubella persistence and highlight the source-sink population structure of the region. We identify countries that contribute to the regional metapopulation network and link epidemic dynamics to susceptibility profiles within each country. Our results indicate that human movement plays an important role in driving epidemic dynamics in E/SE Asia., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Although measles incidence has reached historic lows in many parts of the world, the disease still causes substantial morbidity globally. Even where control programs have succeeded in driving measles locally extinct, unless vaccination coverage is maintained at extremely high levels, susceptible numbers may increase sufficiently to spark large outbreaks. Human mobility will drive potentially infectious contacts and interact with the landscape of susceptibility to determine the pattern of measles outbreaks. These interactions have proved difficult to characterise empirically. We explore the degree to which new sources of data combined with existing public health data can be used to evaluate the landscape of immunity and the role of spatial movement for measles introductions by retrospectively evaluating our ability to predict measles outbreaks in vaccinated populations. Using inferred spatial patterns of accumulation of susceptible individuals and travel data, we predicted the timing of epidemics in each district of Pakistan during a large measles outbreak in 2012-2013 with over 30 000 reported cases. We combined these data with mobility data extracted from over 40 million mobile phone subscribers during the same time frame in the country to quantify the role of connectivity in the spread of measles. We investigate how different approaches could contribute to targeting vaccination efforts to reach districts before outbreaks started. While some prediction was possible, accuracy was low and we discuss key uncertainties linked to existing data streams that impede such inference and detail what data might be necessary to robustly infer timing of epidemics.

Measles is a major cause of childhood morbidity and mortality in many parts of the world. Estimates of the case-fatality rate (CFR) of measles have varied widely from place to place, as well as in the same location over time. Amongst populations that have experienced famine or armed conflict, measles CFR can be especially high, although past work has mostly focused on refugee populations. Here, we estimate measles CFR between 1970 and 1991 in a rural region of Bangladesh, which experienced civil war and famine in the 1970s. We use historical measles mortality data and a mechanistic model of measles transmission to estimate the CFR of measles. We first demonstrate the ability of this model to recover the CFR in the absence of incidence data, using simulated mortality data. Our method produces CFR estimates that correspond closely to independent estimates from surveillance data and we can capture both the magnitude and the change in CFR suggested by these previous estimates. We use this method to quantify the sharp increase in CFR that resulted in a large number of deaths during a measles outbreak in the region in 1976. Most of the children who died during this outbreak were born during a famine in 1974, or in the 2 years preceding the famine. Our results suggest that the period of turmoil during and after the 1971 war and the sustained effects of the famine, is likely to have contributed to the high fatality burden of the 1976 measles outbreak in Matlab.

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting., (Copyright © 2016. Published by Elsevier Ltd.)