Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19

We report key epidemiologic parameter estimates for coronavirus disease identified in peer-reviewed publications, preprint articles, and online reports. Range estimates for incubation period were 1.8–6.9 days, serial interval 4.0–7.5 days, and doubling time 2.3–7.4 days. The effective reproductive number varied widely, with reductions attributable to interventions. Case burden and infection fatality ratios increased with patient age. Implementation of combined interventions could reduce cases and delay epidemic peak up to 1 month. These parameters for transmission, disease severity, and intervention effectiveness are critical for guiding policy decisions. Estimates will likely change as new information becomes available

Malaria and other vector-borne infection surveillance in the U.S. Department of Defense Armed Forces Health Surveillance Center-Global Emerging Infections Surveillance program: review of 2009 accomplishments

Vector-borne infections (VBI) are defined as infectious diseases transmitted by the bite or mechanical transfer of arthropod vectors. They constitute a significant proportion of the global infectious disease burden. United States (U.S.) Department of Defense (DoD) personnel are especially vulnerable to VBIs due to occupational contact with arthropod vectors, immunological naiveté to previously unencountered pathogens, and limited diagnostic and treatment options available in the austere and unstable environments sometimes associated with military operations. In addition to the risk uniquely encountered by military populations, other factors have driven the worldwide emergence of VBIs. Unprecedented levels of global travel, tourism and trade, and blurred lines of demarcation between zoonotic VBI reservoirs and human populations increase vector exposure. Urban growth in previously undeveloped regions and perturbations in global weather patterns also contribute to the rise of VBIs. The Armed Forces Health Surveillance Center-Global Emerging Infections Surveillance and Response System (AFHSC-GEIS) and its partners at DoD overseas laboratories form a network to better characterize the nature, emergence and growth of VBIs globally. In 2009 the network tested 19,730 specimens from 25 sites for Plasmodium species and malaria drug resistance phenotypes and nearly another 10,000 samples to determine the etiologies of non-Plasmodium species VBIs from regions spanning from Oceania to Africa, South America, and northeast, south and Southeast Asia. This review describes recent VBI-related epidemiological studies conducted by AFHSC-GEIS partner laboratories within the OCONUS DoD laboratory network emphasizing their impact on human populations

Recommended reporting items for epidemic forecasting and prediction research : the EPIFORGE 2020 guidelines

Funding: MIDAS Coordination Center and the National Institutes of General Medical Sciences (NIGMS 1U24GM132013) for supporting travel to the face-to-face consensus meeting by members of the Working Group. NGR was supported by the National Institutes of General Medical Sciences (R35GM119582). Travel for SV was supported by the National Institutes of General Medical Sciences (1U24GM132013-01). BMA was supported by Bill & Melinda Gates through the Global Good Fund. RL was funded by a Royal Society Dorothy Hodgkin Fellowship.Background  The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. Methods and findings  We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. Conclusions  These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.Publisher PDFNon peer reviewe

Characterizing micro-scale transmission dynamics of influenza

Influenza is an acute viral respiratory infection that causes a wide range of morbidity and mortality annually. Many unknowns remain regarding tranmission at smaller spatial levels, and the immunological response to infection. These unknowns are due, in part, to lack of statistical approaches that can accurately predict and characterize influenza transmission at smaller spatial scales. In this dissertation, we present an examination of a school-based syndromic indicator for influenza surveillance using negative binomial models to predict reported & confirmed influenza cases over multiple influenza seasons (2007, and 2010-2016) at the county-level. Including all-cause student absences in models of week of year and average weekly temperature improved predictions of reported confirmed influenza cases. Using elementary school absences, and specifically lower grade (K-5) student absences resulted in reduced mean absolute error estimates. We demonstrated that school absences can be a useful predictor and potential early detector of influenza at the community level. Using data from southeast China, we then examined the estimation of incident infections in regions with non-seasonal influenza by comparing a standard seroconversion method to a proposed method that uses antibody titers to multiple historical strains. Applying our adjustment method resulted in decreased seroconversion rates for non-circulating strains, and an increase in seroconversion rates to recently circulating strains. When examining seroconversion to the most recently circulating strain in our study, participants under 15, and over 65 had the highest seroconversion rates to A/Brisbane/10/2007 compared to other age groups. Finally, we examined community and household transmission of influenza-like illness (ILI) using cohort data from households with school-aged children and chain binomial models within a Bayesian Markov Chain Monte Carlo framework. We extended initial transmission models to account for baseline susceptibility and further extended our models to estimate transmission from vaccinated and unvaccinated household members. Models were also applied to estimate transmission of influenza and non- influenza infections. Our models examining transmissibility of ILI and vaccination status found that symptomatic unvaccinated household members had 2.7 (95% confidence interval (CI): 1.03, 7.88) times the odds of being more infectious than symptomatic vaccinated household members. The findings presented here improve the detection of incident infections using infection proxies, such as student absences, influenza-like illness, and infections based on serology, and add to currently available methodologies for the estimation of transmission at the household- and community-level

Characterizing micro-scale transmission dynamics of influenza

Influenza is an acute viral respiratory infection that causes a wide range of morbidity and mortality annually. Many unknowns remain regarding tranmission at smaller spatial levels, and the immunological response to infection. These unknowns are due, in part, to lack of statistical approaches that can accurately predict and characterize influenza transmission at smaller spatial scales. In this dissertation, we present an examination of a school-based syndromic indicator for influenza surveillance using negative binomial models to predict reported & confirmed influenza cases over multiple influenza seasons (2007, and 2010-2016) at the county-level. Including all-cause student absences in models of week of year and average weekly temperature improved predictions of reported confirmed influenza cases. Using elementary school absences, and specifically lower grade (K-5) student absences resulted in reduced mean absolute error estimates. We demonstrated that school absences can be a useful predictor and potential early detector of influenza at the community level. Using data from southeast China, we then examined the estimation of incident infections in regions with non-seasonal influenza by comparing a standard seroconversion method to a proposed method that uses antibody titers to multiple historical strains. Applying our adjustment method resulted in decreased seroconversion rates for non-circulating strains, and an increase in seroconversion rates to recently circulating strains. When examining seroconversion to the most recently circulating strain in our study, participants under 15, and over 65 had the highest seroconversion rates to A/Brisbane/10/2007 compared to other age groups. Finally, we examined community and household transmission of influenza-like illness (ILI) using cohort data from households with school-aged children and chain binomial models within a Bayesian Markov Chain Monte Carlo framework. We extended initial transmission models to account for baseline susceptibility and further extended our models to estimate transmission from vaccinated and unvaccinated household members. Models were also applied to estimate transmission of influenza and non- influenza infections. Our models examining transmissibility of ILI and vaccination status found that symptomatic unvaccinated household members had 2.7 (95% confidence interval (CI): 1.03, 7.88) times the odds of being more infectious than symptomatic vaccinated household members. The findings presented here improve the detection of incident infections using infection proxies, such as student absences, influenza-like illness, and infections based on serology, and add to currently available methodologies for the estimation of transmission at the household- and community-level

Viral etiology and seasonal trends of pediatric acute febrile illness in southern Puerto Rico; a seven-year review.

BackgroundAcute febrile illness (AFI) is an important cause for seeking health care among children. Knowledge of the most common etiologic agents of AFI and its seasonality is limited in most tropical regions.Methodology/principal findingsTo describe the viral etiology of AFI in pediatric patients (≤18 years) recruited through a sentinel enhanced dengue surveillance system (SEDSS) in Southern Puerto Rico, we analyzed data for patients enrolled from 2012 to May 2018. To identify seasonal patterns, we applied time-series analyses to monthly arboviral and respiratory infection case data. We calculated coherence and phase differences for paired time-series to quantify the association between each time series. A viral pathogen was found in 47% of the 14,738 patients. Influenza A virus was the most common pathogen detected (26%). The incidence of Zika and dengue virus etiologies increased with age. Arboviral infections peaked between June and September throughout the times-series. Respiratory infections have seasonal peaks occurring in the fall and winter months of each year, though patterns vary by individual respiratory pathogen.Conclusions/significanceDistinct seasonal patterns and differences in relative frequency by age groups seen in this study can guide clinical and laboratory assessment in pediatric patients with AFI in Puerto Rico

Reduced spread of influenza and other respiratory viral infections during the COVID-19 pandemic in southern Puerto Rico.

IntroductionImpacts of COVID-19 mitigation measures on seasonal respiratory viruses is unknown in sub-tropical climates.MethodsWe compared weekly testing and test-positivity of respiratory infections in the 2019-2020 respiratory season to the 2012-2018 seasons in southern Puerto Rico using Wilcoxon signed rank tests.ResultsCompared to the average for the 2012-2018 seasons, test-positivity was significantly lower for Influenza A (pConclusionsMitigation measures and behavioral social distancing choices may have reduced respiratory viral spread in southern Puerto Rico

Household transmission dynamics of seasonal human coronaviruses

Background Household transmission studies inform how viruses spread among close contacts, but few characterize household transmission of endemic coronaviruses. Methods We used data collected from 223 households with school-age children participating in weekly disease surveillance over 2 respiratory virus seasons (December 2015 to May 2017), to describe clinical characteristics of endemic human coronaviruses (HCoV-229E, HcoV-HKU1, HcoV-NL63, HcoV-OC43) infections, and community and household transmission probabilities using a chain-binomial model correcting for missing data from untested households. Results Among 947 participants in 223 households, we observed 121 infections during the study, most commonly subtype HCoV-OC43. Higher proportions of infected children (<19 years) displayed influenza-like illness symptoms than infected adults (relative risk, 3.0; 95% credible interval [CrI], 1.5–6.9). The estimated weekly household transmission probability was 9% (95% CrI, 6–13) and weekly community acquisition probability was 7% (95% CrI, 5–10). We found no evidence for differences in community or household transmission probabilities by age or symptom status. Simulations suggest that our study was underpowered to detect such differences. Conclusions Our study highlights the need for large household studies to inform household transmission, the challenges in estimating household transmission probabilities from asymptomatic individuals, and implications for controlling endemic CoVs

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.

Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection