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Abstract Background The Covid-19 pandemic has been characterized by the emergence of novel SARS-CoV-2 variants, each with distinct properties influencing transmission dynamics, immune escape, and virulence, which, in turn, influence their impact on local populations. Swift analysis of the properties of newly emerged variants is essential in the initial days and weeks to enhance readiness and facilitate the scaling of clinical and public health system responses. Methods This paper introduces a two-variant metapopulation compartmental model of disease transmission to simulate the dynamics of disease transmission during a period of transition to a newly dominant strain. Leveraging novel S-gene dropout analysis data and genomic sequencing data, combined with confirmed Covid-19 case data, we estimate the epidemiological characteristics of the Omicron variant, which replaced the Delta variant in late 2021 in Philadelphia, PA. We utilized a grid-search method to identify plausible combinations of model parameters, followed by an ensemble adjustment Kalman filter for parameter inference. Results The model successfully estimated key epidemiological parameters; we estimated the ascertainment rate of 0.22 (95% credible interval 0.15–0.29) and transmission rate of 5.0 (95% CI 2.4–6.6) for the Omicron variant. Conclusions The study demonstrates the potential for this model-inference framework to provide real-time insights during the emergence of novel variants, aiding in timely public health responses.

Abstract Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Searches for “pro-suicide” websites in the United States peaked during the week a high-profile news story was published and remained elevated for 6 months afterward, highlighting the need to avoid mentioning specific sources of explicit suicide instructions in media publications.

Abstract: Ischemic stroke is a major cause of mortality worldwide; however, few studies have been conducted to measure the impact of the distribution of healthcare services on ischemic stroke fatality. This study aimed to explore the relationship between three ischemic stroke outcomes (incidence, mortality, and fatality) and accessibility to hospitals in Spain, considering its economic development. A cross-sectional ecological study was performed using data on hospital admissions and mortality due to ischemic stroke during 2016-2018. Gross geographic product (GGP) per capita was estimated and a healthcare accessibility index was created. A Besag-York-Mollié autoregressive spatial model was used to estimate the magnitude of association between ischemic stroke outcomes and economic development and healthcare accessibility. GGP per capita showed a geographical gradient from southwest to northeast in Spain. Mortality and case-fatality rates due to ischemic stroke were higher in the south of the country in both women and men aged 60+ years. In women and men aged 20-59 years a EUR 1,000 increase in GGP per capita was associated with decreases in mortality of 5% and 4%, respectively. Fatality decreased 3-4% with each EUR 1,000 increase of GGP per capita in both sexes and in the 20-59 and 60+ age groups. Decreased healthcare accessibility was associated with higher fatality in the population aged 60+. Economic development in southwest Spain would not only improve employment opportunities but also reduce ischemic stroke mortality. New health related strategies to improve hospital accessibility should be considered in more sparsely populated regions or those with worse transport and/or healthcare infrastructure.

Background The rate of suicide death has been increasing, making understanding risk factors of growing importance. While exposure to explicit suicide-related media, such as description of means in news reports or sensationalized fictional portrayal, is known to increase population suicide rates, it is not known whether prosuicide website forums, which often promote or facilitate information about fatal suicide means, are related to change in suicide deaths overall or by specific means. Objective This study aimed to estimate the association of the frequency of Google searches of known prosuicide web forums and content with death by suicide over time in the United States, by age, sex, and means of death. Methods National monthly Google search data for names of common prosuicide websites between January 2010 and December 2021 were extracted from Google Health Trends API (application programming interface). Suicide deaths were identified using the CDC (Centers for Disease Control and Prevention) National Vital Statistics System (NVSS), and 3 primary means of death were identified (poisoning, suffocation, and firearm). Distributed lag nonlinear models (DLNMs) were then used to estimate the lagged association between the number of Google searches on suicide mortality, stratified by age, sex, and means, and adjusted for month. Sensitivity analyses, including using autoregressive integrated moving average (ARIMA) modeling approaches, were also conducted. Results Months in the United States in which search rates for prosuicide websites increased had more documented deaths by intentional poisoning and suffocation among both adolescents and adults. For example, the risk of poisoning suicide among youth and young adults (age 10-24 years) was 1.79 (95% CI 1.06-3.03) times higher in months with 22 searches per 10 million as compared to 0 searches. The risk of poisoning suicide among adults aged 25-64 was 1.10 (95% CI 1.03-1.16) times higher 1 month after searches reached 9 per 10 million compared with 0 searches. We also observed that increased search rates were associated with fewer youth suicide deaths by firearms with a 3-month time lag for adolescents. These models were robust to sensitivity tests. Conclusions Although more analysis is needed, the findings are suggestive of an association between increased prosuicide website access and increased suicide deaths, specifically deaths by poisoning and suffocation. These findings emphasize the need to further investigate sites containing potentially dangerous information and their associations with deaths by suicide, as they may affect vulnerable individuals.

Abstract Importance Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. Objective To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. Methods We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. Results The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation – compared with young adults (the reference group,24 years old 65 years old) were lower by 12.1% (95%CI: 11.1% – 13.3%), and the completion rate for youth group (age

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Abstract Background Understanding community transmission of SARS-CoV-2 variants of concern (VOCs) is critical for disease control in the post pandemic era. The Delta variant (B.1.617.2) emerged in late 2020 and became the dominant VOC globally in the summer of 2021. While the epidemiological features of the Delta variant have been extensively studied, how those characteristics shaped community transmission in urban settings remains poorly understood. Methods Using high-resolution contact tracing data and testing records, we analyze the transmission of SARS-CoV-2 during the Delta wave within New York City (NYC) from May 2021 to October 2021. We reconstruct transmission networks at the individual level and across 177 ZIP code areas, examine network structure and spatial spread patterns, and use statistical analysis to estimate the effects of factors associated with COVID-19 spread. Results We find considerable individual variations in reported contacts and secondary infections, consistent with the pre-Delta period. Compared with earlier waves, Delta-period has more frequent long-range transmission events across ZIP codes. Using socioeconomic, mobility and COVID-19 surveillance data at the ZIP code level, we find that a larger number of cumulative cases in a ZIP code area is associated with reduced within- and cross-ZIP code transmission and the number of visitors to each ZIP code is positively associated with the number of non-household infections identified through contact tracing and testing. Conclusions The Delta variant produced greater long-range spatial transmission across NYC ZIP code areas, likely caused by its increased transmissibility and elevated human mobility during the study period. Our findings highlight the potential role of population immunity in reducing transmission of VOCs. Quantifying variability of immunity is critical for identifying subpopulations susceptible to future VOCs. In addition, non-pharmaceutical interventions limiting human mobility likely reduced SARS-CoV-2 spread over successive pandemic waves and should be encouraged for reducing transmission of future VOCs.

Abstract Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

In the United States, tropical cyclones cause destructive flooding that can lead to adverse health outcomes. Storm-driven flooding contaminates environmental, recreational, and drinking water sources, but few studies have examined effects on specific infections over time. We used 23 years of exposure and case data to assess the effects of tropical cyclones on 6 waterborne diseases in a conditional quasi-Poisson model. We separately defined storm exposure for windspeed, rainfall, and proximity to the storm track. Exposure to storm-related rainfall was associated with a 48% (95% CI 27%–69%) increase in Shiga toxin–producing Escherichia coli infections 1 week after storms and a 42% (95% CI 22%–62%) in increase Legionnaires’ disease 2 weeks after storms. Cryptosporidiosis cases increased 52% (95% CI 42%–62%) during storm weeks but declined over ensuing weeks. Cyclones are a risk to public health that will likely become more serious with climate change and aging water infrastructure systems.

Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what 'correct' really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

Surveillance of COVID-19 is challenging but critical for mitigating disease, particularly if predictive of future disease burden. We report a robust multiyear lead-lag association between internet search activity for loss of smell or taste and COVID-19–associated hospitalization and deaths. These search data could help predict COVID-19 surges.

The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.

In this study, the authors analyse contact tracing records for ~650,000 suspected or confirmed COVID-19 cases in New York City during the second epidemic wave. They reconstruct transmission networks and find that vaccination and zone-based control policies likely contributed to control of the epidemic.

Coronavirus disease 2019 (COVID-19) will likely remain a major public health burden; accurate forecast of COVID-19 epidemic outcomes several months into the future is needed to support more proactive planning. Here, we propose strategies to address three major forecast challenges, i.e., error growth, the emergence of new variants, and infection seasonality. Using these strategies in combination we generate retrospective predictions of COVID-19 cases and deaths 6 months in the future for 10 representative US states. Tallied over >25,000 retrospective predictions through September 2022, the forecast approach using all three strategies consistently outperformed a baseline forecast approach without these strategies across different variant waves and locations, for all forecast targets. Overall, probabilistic forecast accuracy improved by 64% and 38% and point prediction accuracy by 133% and 87% for cases and deaths, respectively. Real-time 6-month lead predictions made in early October 2022 suggested large attack rates in most states but a lower burden of deaths than previous waves during October 2022 -March 2023; these predictions are in general accurate compared to reported data. The superior skill of the forecast methods developed here demonstrate means for generating more accurate long-lead forecast of COVID-19 and possibly other infectious diseases.

Abstract Background An increasing severity of extreme storms and more intense seasonal flooding are projected consequences of climate change in the United States. In addition to the immediate destruction caused by storm surges and catastrophic flooding, these events may also increase the risk of infectious disease transmission. We aimed to determine the association between extreme and seasonal floods and hospitalizations for Legionnaires’ disease in 25 US states during 2000–2011. Methods We used a nonparametric bootstrap approach to examine the association between Legionnaires’ disease hospitalizations and extreme floods, defined by multiple hydrometeorological variables. We also assessed the effect of extreme flooding associated with named cyclonic storms on hospitalizations in a generalized linear mixed model (GLMM) framework. To quantify the effect of seasonal floods, we used multi-model inference to identify the most highly weighted flood-indicator variables and evaluated their effects on hospitalizations in a GLMM. Results We found a 32% increase in monthly hospitalizations at sites that experienced cyclonic storms, compared to sites in months without storms. Hospitalizations in months with extreme precipitation were in the 89th percentile of the bootstrapped distribution of monthly hospitalizations. Soil moisture and precipitation were the most highly weighted variables identified by multi-model inference and were included in the final model. A 1-standard deviation (SD) increase in average monthly soil moisture was associated with a 49% increase in hospitalizations; in the same model, a 1-SD increase in precipitation was associated with a 26% increase in hospitalizations. Conclusions This analysis is the first to examine the effects of flooding on hospitalizations for Legionnaires’ disease in the United States using a range of flood-indicator variables and flood definitions. We found evidence that extreme and seasonal flooding is associated with increased hospitalizations; further research is required to mechanistically establish whether floodwaters contaminated with Legionella bacteria drive transmission.

Deaths by suicide, as well as suicidal ideations, plans and attempts, have been increasing in the US for the past two decades. Deployment of effective interventions would require timely, geographically well-resolved estimates of suicide activity. In this study, we evaluated the feasibility of a two-step process for predicting suicide mortality: a) generation of hindcasts, mortality estimates for past months for which observational data would not have been available if forecasts were generated in real-time; and b) generation of forecasts with observational data augmented with hindcasts. Calls to crisis hotline services and online queries to the Google search engine for suicide-related terms were used as proxy data sources to generate hindcasts. The primary hindcast model (auto) is an Autoregressive Integrated Moving average model (ARIMA), trained on suicide mortality rates alone. Three regression models augment hindcast estimates from auto with call rates (calls), GHT search rates (ght) and both datasets together (calls_ght). The 4 forecast models used are ARIMA models trained with corresponding hindcast estimates. All models were evaluated against a baseline random walk with drift model. Rolling monthly 6-month ahead forecasts for all 50 states between 2012 and 2020 were generated. Quantile score (QS) was used to assess the quality of the forecast distributions. Median QS for auto was better than baseline (0.114 vs. 0.21. Median QS of augmented models were lower than auto, but not significantly different from each other (Wilcoxon signed-rank test, p > .05). Forecasts from augmented models were also better calibrated. Together, these results provide evidence that proxy data can address delays in release of suicide mortality data and improve forecast quality. An operational forecast system of state-level suicide risk may be feasible with sustained engagement between modelers and public health departments to appraise data sources and methods as well as to continuously evaluate forecast accuracy.

By August 1, 2022, the SARS-CoV-2 virus had caused over 90 million cases of COVID-19 and one million deaths in the United States. Since December 2020, SARS-CoV-2 vaccines have been a key component of US pandemic response; however, the impacts of vaccination are not easily quantified. Here, we use a dynamic county-scale metapopulation model to estimate the number of cases, hospitalizations, and deaths averted due to vaccination during the first six months of vaccine availability. We estimate that COVID-19 vaccination was associated with over 8 million fewer confirmed cases, over 120 thousand fewer deaths, and 700 thousand fewer hospitalizations during the first six months of the campaign.

Summary: Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5–11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5–11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5–11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880–0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834–0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797–1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5–11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments).

Quantification of the transmissibility and immune escape properties of SARS-CoV-2 variants is necessary to support pandemic planning. Here, the authors develop a model inference system to estimate these properties using incidence and mortality data for three variants of concern.

The role of meteorological factors in SARS-COV-2 transmission is not well understood. Here, the authors use county-level data from the United States to the end of 2020 and find evidence of a moderate association between increased transmissibility and cold, dry weather and low ultraviolet radiation.

Given the crucial role of climate in malaria transmission, many mechanistic models of malaria represent vector biology and the parasite lifecycle as functions of climate variables in order to accurately capture malaria transmission dynamics. Lower dimension mechanistic models that utilize implicit vector dynamics have relied on indirect climate modulation of transmission processes, which compromises investigation of the ecological role played by climate in malaria transmission. In this study, we develop an implicit process-based malaria model with direct climate-mediated modulation of transmission pressure borne through the Entomological Inoculation Rate (EIR). The EIR, a measure of the number of infectious bites per person per unit time, includes the effects of vector dynamics, resulting from mosquito development, survivorship, feeding activity and parasite development, all of which are moderated by climate. We combine this EIR-model framework, which is driven by rainfall and temperature, with Bayesian inference methods, and evaluate the model's ability to simulate local transmission across 42 regions in Rwanda over four years. Our findings indicate that the biologically-motivated, EIR-model framework is capable of accurately simulating seasonal malaria dynamics and capturing of some of the inter-annual variation in malaria incidence. However, the model unsurprisingly failed to reproduce large declines in malaria transmission during 2018 and 2019 due to elevated anti-malaria measures, which were not accounted for in the model structure. The climate-driven transmission model also captured regional variation in malaria incidence across Rwanda's diverse climate, while identifying key entomological and epidemiological parameters important to seasonal malaria dynamics. In general, this new model construct advances the capabilities of implicitly-forced lower dimension dynamical malaria models by leveraging climate drivers of malaria ecology and transmission.

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

Lack of a widespread surveillance network hampers accurate infectious disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory diseases for locations without surveillance is feasible.

Here, Heaney et al. show that La Niña conditions are associated with higher than average incidence of childhood diarrheal disease in Botswana in the early rainy season. This finding could help to predict childhood diarrhea outbreaks in southern Africa.

BackgroundWith the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.Methods and findingsCounty-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran's I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%-29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37-49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34-44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.ConclusionsSignificant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

Forecasting is beginning to be integrated into decision-making processes for infectious disease outbreak response. We discuss how technologies could accelerate the adoption of forecasting among public health practitioners, improve epidemic management, save lives, and reduce the economic impact of outbreaks.

Seasonal influenza in the United States is estimated to cause 9–35 million illnesses annually, with resultant economic burden amounting to $47-$150 billion. Reliable real-time forecasts of influenza can help public health agencies better manage these outbreaks. Here, we investigate the feasibility of three autoregressive methods for near-term forecasts: an Autoregressive Integrated Moving Average (ARIMA) model with time-varying order; an ARIMA model fit to seasonally adjusted incidence rates (ARIMA-STL); and a feed-forward autoregressive artificial neural network with a single hidden layer (AR-NN). We generated retrospective forecasts for influenza incidence one to four weeks in the future at US National and 10 regions in the US during 5 influenza seasons. We compared the relative accuracy of the point and probabilistic forecasts of the three models with respect to each other and in relation to two large external validation sets that each comprise at least 20 other models.Both the probabilistic and point forecasts of AR-NN were found to be more accurate than those of the other two models overall. An additional sub-analysis found that the three models benefitted considerably from the use of search trends based 'nowcast' as a proxy for surveillance data, and these three models with use of nowcasts were found to be the highest ranked models in both validation datasets. When the nowcasts were withheld, the three models remained competitive relative to models in the validation sets. The difference in accuracy among the three models, and relative to models of the validation sets, was found to be largely statistically significant.Our results suggest that autoregressive models even when not equipped to capture transmission dynamics can provide reasonably accurate near-term forecasts for influenza. Existing support in open-source libraries make them suitable non-naïve baselines for model comparison studies and for operational forecasts in resource constrained settings where more sophisticated methods may not be feasible. Keywords: Influenza-like illness, Forecasts, Near-term incidence, Auto-regressive models, Neural networks

Respiratory syncytial virus (RSV) infections peak during the winter months in the United States, yet the timing, intensity, and onset of these outbreaks vary each year. An RSV vaccine is on the cusp of being released; precise models and accurate forecasts of RSV epidemics may prove vital for planning where and when the vaccine should be deployed. Accurate forecasts with sufficient spatial and temporal resolution could also be used to support the prevention or treatment of RSV infections. Previously, we developed and validated an RSV forecast system at the regional scale in the United States. This model-inference system had considerable forecast skill, relative to the historical expectance, for outbreak peak intensity, total outbreak size, and onset, but only marginal skill for predicting the timing of the outbreak peak. Here, we use a superensemble approach to combine three forecasting methods for RSV prediction in the US at three different spatial resolutions: national, regional, and state. At the regional and state levels, we find a substantial improvement of forecast skill, relative to historical expectance, for peak intensity, timing, and onset outbreak up to two months in advance of the predicted outbreak peak. Moreover, due to the greater variability of RSV outbreaks at finer spatial scales, we find that improvement of forecast skill at the state level exceeds that at the regional and national levels. Such finer scale superensemble forecasts may be more relevant for effecting local-scale interventions, particularly in communities with a high burden of RSV infection. Keywords: RSV, Superensemble, Forecast, Infectious disease, Bayesian modeling, Outbreaks, Epidemics, Computational biology

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value

Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.

Past work has shown that models incorporating human travel can improve the quality of influenza forecasts. Here, we develop and validate a metapopulation model of twelve European countries, in which international translocation of virus is driven by observed commuting and air travel flows, and use this model to generate influenza forecasts in conjunction with incidence data from the World Health Organization. We find that, although the metapopulation model fits the data well, it offers no improvement over isolated models in forecast quality. We discuss several potential reasons for these results. In particular, we note the need for data that are more comparable from country to country, and offer suggestions as to how surveillance systems might be improved to achieve this goal.

Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy. Keywords: Infectious disease model, Infectious disease forecasting, Vector-borne disease, Disease surveillance data, Dengue, Zika, Mosquito-borne disease

Diarrheal disease is the second largest cause of mortality in children younger than 5, yet our ability to anticipate and prepare for outbreaks remains limited. Here, we develop and test an epidemiological forecast model for childhood diarrheal disease in Chobe District, Botswana. Our prediction system uses a compartmental susceptible-infected-recovered-susceptible (SIRS) model coupled with Bayesian data assimilation to infer relevant epidemiological parameter values and generate retrospective forecasts. Our model inferred two system parameters and accurately simulated weekly observed diarrhea cases from 2007-2017. Accurate retrospective forecasts for diarrhea outbreaks were generated up to six weeks before the predicted peak of the outbreak, and accuracy increased over the progression of the outbreak. Many forecasts generated by our model system were more accurate than predictions made using only historical data trends. Accurate real-time forecasts have the potential to increase local preparedness for coming outbreaks through improved resource allocation and healthcare worker distribution. Keywords: Childhood diarrhea, Forecasting, Bayesian inference, Dynamic modeling

Yearly increase in influenza activity is associated with cold and dry winter in the temperate regions, while influenza patterns in tropical countries vary significantly by regional climates and geographic locations. To examine the association between influenza activity in Thailand and local climate factors including temperature, relative humidity, and rainfall, we analyzed the influenza surveillance data from January 2010 to December 2018 obtained from a large private hospital in Bangkok. We found that approximately one in five influenza-like illness samples (21.6% or 6,678/30,852) tested positive for influenza virus. Influenza virus typing showed that 34.2% were influenza A(H1N1)pdm09, 46.0% were influenza A(H3N2), and 19.8% were influenza B virus. There were two seasonal waves of increased influenza activity. Peak influenza A(H1N1)pdm09 activity occurred in February and again in August, while influenza A(H3N2) and influenza B viruses were primarily detected in August and September. Time series analysis suggests that increased relative humidity was significantly associated with increased influenza activity in Bangkok. Months with peak influenza activity generally followed the most humid months of the year. We performed the seasonal autoregressive integrated moving average (SARIMA) multivariate analysis of all influenza activity on the 2011 to 2017 data to predict the influenza activity for 2018. The resulting model closely resembled the actual observed overall influenza detected that year. Consequently, the ability to predict seasonal pattern of influenza in a large tropical city such as Bangkok may enable better public health planning and underscores the importance of annual influenza vaccination prior to the rainy season.

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.

In this paper, we report the epidemic characteristics of the three co-circulating influenza viruses (i.e., A/H1N1, A/H3N2, and B) in two tropical African cities—Kampala and Entebbe, Uganda—over an eight-year period (2008–2015). Using wavelet methods, we show that influenza epidemics recurred annually during the study period. In most months, two or more influenza viruses co-circulated at the same time. However, the epidemic timing differed by influenza (sub)type. Influenza A/H3N2 caused epidemics approximately every 2 years in both cities and tended to alternate with A/H1N1 or B. Influenza A/H1N1 and B produced smaller but more frequent epidemics and biennial epidemics of these two viruses tended to be synchronous. In addition, epidemics of A/H3N2 were more synchronized in the two cities (located ca.37 km apart) than that of A/H1N1 or influenza B. Keywords: Influenza, Tropical regions, (sub)type specific, Uganda, Epidemic frequency

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014–15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1–4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1.Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from

Abstract Background The emergence of West Nile virus (WNV) in the Western Hemisphere has motivated research into the processes contributing to the incidence and persistence of the disease in the region. Meteorology and hydrology are fundamental determinants of vector-borne disease transmission dynamics of a region. The availability of water influences the population dynamics of vector and host, while temperature impacts vector growth rates, feeding habits, and disease transmission potential. Characterization of the temporal pattern of environmental factors influencing WNV risk is crucial to broaden our understanding of local transmission dynamics and to inform efforts of control and surveillance. Methods We used hydrologic, meteorological and WNV data from west Texas (2002–2016) to analyze the relationship between environmental conditions and annual human WNV infection. A Bayesian model averaging framework was used to evaluate the association of monthly environmental conditions with WNV infection. Results Findings indicate that wet conditions in the spring combined with dry and cool conditions in the summer are associated with increased annual WNV cases. Bayesian multi-model inference reveals monthly means of soil moisture, specific humidity and temperature to be the most important variables among predictors tested. Environmental conditions in March, June, July and August were the leading predictors in the best-fitting models. Conclusions The results significantly link soil moisture and temperature in the spring and summer to WNV transmission risk. Wet spring in association with dry and cool summer was the temporal pattern best-describing WNV, regardless of year. Our findings also highlight that soil moisture may be a stronger predictor of annual WNV transmission than rainfall.

While influenza has been simulated extensively to better understand its behavior and predict future outbreaks, most other respiratory viruses have seldom been simulated. In this study, we provide an overview of four common respiratory viral infections: respiratory syncytial virus (RSV), respiratory adenovirus, rhinovirus and parainfluenza, present specimen data collected 2004–2014, and simulate outbreaks in 19 overlapping regions in the United States. Pairing a compartmental model and data assimilation methods, we infer key epidemiological parameters governing transmission: the basic reproductive number R0 and length of infection D. RSV had been previously simulated, and our mean estimate of D and R0 of 5.2 days and 2.8, respectively, are within published clinical and modeling estimates. Among the four virus groupings, mean estimates of R0 range from 2.3 to 3.0, with a lower and upper quartile range of 2.0–2.8 and 2.6–3.2, respectively. As rapid PCR testing becomes more common, estimates of the observed virulence and duration of infection for these viruses could inform decision making by clinicians and officials for managing patient treatment and response.

Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

Since its introduction to the US in 1999, the West Nile virus (WNV) has become endemic in the Americas. Here, the authors develop a model of WNV transmission dynamics between birds, mosquitoes and humans, which they integrate in conjunction with data assimilation methods, mosquito infection data and reported human cases in a New York county to show its utility for forecasting infection rates.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

Estimation of influenza-like illness (ILI) using search trends activity was intended to supplement traditional surveillance systems, and was a motivation behind the development of Google Flu Trends (GFT). However, several studies have previously reported large errors in GFT estimates of ILI in the US. Following recent release of time-stamped surveillance data, which better reflects real-time operational scenarios, we reanalyzed GFT errors. Using three data sources-GFT: an archive of weekly ILI estimates from Google Flu Trends; ILIf: fully-observed ILI rates from ILINet; and, ILIp: ILI rates available in real-time based on partial reporting-five influenza seasons were analyzed and mean square errors (MSE) of GFT and ILIp as estimates of ILIf were computed. To correct GFT errors, a random forest regression model was built with ILI and GFT rates from the previous three weeks as predictors. An overall reduction in error of 44% was observed and the errors of the corrected GFT are lower than those of ILIp. An 80% reduction in error during 2012/13, when GFT had large errors, shows that extreme failures of GFT could have been avoided. Using autoregressive integrated moving average (ARIMA) models, one- to four-week ahead forecasts were generated with two separate data streams: ILIp alone, and with both ILIp and corrected GFT. At all forecast targets and seasons, and for all but two regions, inclusion of GFT lowered MSE. Results from two alternative error measures, mean absolute error and mean absolute proportional error, were largely consistent with results from MSE. Taken together these findings provide an error profile of GFT in the US, establish strong evidence for the adoption of search trends based 'nowcasts' in influenza forecast systems, and encourage reevaluation of the utility of this data source in diverse domains.

[This corrects the article DOI: 10.1371/journal.pcbi.1005474.].

Avian influenza viruses (AIVs) are a continued threat to global health and economy. Unlike other highly pathogenic AIVs, novel H5N8 disseminated very quickly from Korea to other areas in Asia, Europe and even North America following its first outbreak in 2014. However, the pathobiological features of the virus that favoured its global translocation remain unknown. In this study, we used a compartmental model to examine the avian epidemiological characteristics that would support the geographical spread of influenza by bird migration, and to provide recommendations for AIV surveillance in wild bird populations. We simulated virus transmission and translocation in a migratory bird population while varying four system properties: (i) contact transmission rate; (ii) infection recovery rate; (iii) mortality rate induced by infection; and (iv) migratory recovery rate. Using these simulations, we then calculated extinction and translocation probabilities for influenza during spring migration as a function of the altered properties. We find that lower infection recovery rates increase the likelihood of AIV translocation in migratory bird populations. In addition, lower mortality rates or migration recovery rates also favour translocation. Our results identify pathobiological features supporting AIV intercontinental dissemination risk and suggest that characteristic differences exist among H5N8 and other AIV subtypes that have not translocated as rapidly (e.g. H5N6 and H5N1).

Measles is a highly infectious, severe viral disease. The disease is targeted for global eradication; however, this result has proven challenging. In China, where countrywide vaccination coverage for the last decade has been above 95% (the threshold for measles elimination), measles continues to cause large epidemics. To diagnose factors contributing to the persistency of measles, here we develop a model-inference system to infer measles transmission dynamics in China. The model-inference system uses demographic and vaccination data for each year as model inputs to directly account for changing population dynamics (including births, deaths, migrations, and vaccination). In addition, it simultaneously estimates unobserved model variables and parameters based on incidence data. When fitted to yearly incidence data for the entire population, it is able to accurately estimate independent, out-of-sample age-specific incidence. Using this validated model-inference system, we are thus able to estimate epidemiological and demographical characteristics key to measles transmission during 1951-2004 for three key locations in China, including its capital Beijing. These characteristics include age-specific population susceptibility and incidence rates, the basic reproductive number (R0), reporting rate, population mixing intensity, and amplitude of seasonality. Key differences among the three sites reveal population and epidemiological characteristics crucial for understanding the current persistence of measles epidemics in China. We also discuss the implications our findings have for future elimination strategies.