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Dengue virus (DENV) infection risk is known to vary substantially, even across small communities, with infections in and around the home driving transmission. However, It remains unclear how the immune status of an individual or household dictate this risk in part due to transmission being dominated by subclinical infections. In this study, we used demographic, household characteristic, and serological data from a multigenerational cohort study of 2860 individuals from 470 households in Kamphaeng Phet, Thailand, to determine the incidence and risk factors for DENV infections. We used hemagglutination inhibition (HAI) antibody titers measured in sequential serum samples to identify subclinical infections through a gradient boosted regression model. This approach identified ∼10% more cases than commonly used methods with approximately 90% of all infections being subclinical. As expected, we found that having higher DENV antibody titers was protective against infection. Individuals were additionally protected if other household members had higher titers suggesting that there are indirect effects of household immunity on the individuals found within a household. Our study provides a framework for inferring subclinical infections and characterizing the epidemiology of DENV infection in households.

Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract