Leveraging insect-specific viruses to elucidate mosquito population structure and dynamics.

Several aspects of mosquito ecology that are important for vectored disease transmission and control have been difficult to measure at epidemiologically important scales in the field. In particular, the ability to describe mosquito population structure and movement rates has been hindered by difficulty in quantifying fine-scale genetic variation among populations. The mosquito virome represents a possible avenue for quantifying population structure and movement rates across multiple spatial scales. Mosquito viromes contain a diversity of viruses, including several insect-specific viruses (ISVs) and "core" viruses that have high prevalence across populations. To date, virome studies have focused on viral discovery and have only recently begun examining viral ecology. While nonpathogenic ISVs may be of little public health relevance themselves, they provide a possible route for quantifying mosquito population structure and dynamics. For example, vertically transmitted viruses could behave as a rapidly evolving extension of the host's genome. It should be possible to apply established analytical methods to appropriate viral phylogenies and incidence data to generate novel approaches for estimating mosquito population structure and dispersal over epidemiologically relevant timescales. By studying the virome through the lens of spatial and genomic epidemiology, it may be possible to investigate otherwise cryptic aspects of mosquito ecology. A better understanding of mosquito population structure and dynamics are key for understanding mosquito-borne disease ecology and methods based on ISVs could provide a powerful tool for informing mosquito control programs.

Evolution of Spatial Risk of Malaria Infection After a Pragmatic Chemoprevention Program in Response to Severe Flooding in Rural Western Uganda.

BACKGROUND: Malaria epidemics result from extreme precipitation and flooding, which are increasing with global climate change. Local adaptation and mitigation strategies will be essential to prevent excess morbidity and mortality. METHODS: We investigated the spatial risk of malaria infection at multiple timepoints after severe flooding in rural western Uganda employing longitudinal household surveys measuring parasite prevalence and leveraging remotely sensed information to inform spatial models of malaria risk in the 3 months after flooding. RESULTS: We identified clusters of malaria risk emerging in areas (1) that showed the greatest changes in Normalized Difference Vegetation Index from pre- to postflood and (2) where residents were displaced for longer periods of time and had lower access to long-lasting insecticidal nets, both of which were associated with a positive malaria rapid diagnostic test result. The disproportionate risk persisted despite a concurrent chemoprevention program that achieved high coverage. CONCLUSIONS: The findings enhance our understanding not only of the spatial evolution of malaria risk after flooding, but also in the context of an effective intervention. The results provide a "proof of concept" for programs aiming to prevent malaria outbreaks after flooding using a combination of interventions. Further study of mitigation strategies-and particularly studies of implementation-is urgently needed.

Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda.

BACKGROUND: Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey. METHODS: The results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model's ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated. RESULTS: Models fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier. CONCLUSIONS: These results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to transmission regularly occurring outside of the home. Additionally, they suggest that when predicting malaria risk the benefit may not outweigh the high costs of attaining detailed information on household predictors. Instead, using remotely-sensed data provides an equally effective, cost-efficient alternative.

Dihydroartemisinin-Piperaquine Chemoprevention and Malaria Incidence After Severe Flooding: Evaluation of a Pragmatic Intervention in Rural Uganda.

BACKGROUND: Malaria epidemics are a well-described phenomenon after extreme precipitation and flooding. Yet, few studies have examined mitigation measures to prevent post-flood malaria epidemics. METHODS: We evaluated a malaria chemoprevention program implemented in response to severe flooding in western Uganda. Children aged ≤12 years from 1 village were eligible to receive 3 monthly rounds of dihydroartemisinin-piperaquine (DP). Two neighboring villages served as controls. Malaria cases were defined as individuals with a positive rapid diagnostic test result as recorded in health center registers. We performed a difference-in-differences analysis to estimate changes in the incidence and test positivity of malaria between intervention and control villages. RESULTS: A total of 554 children received at least 1 round of chemoprevention, with 75% participating in at least 2 rounds. Compared with control villages, we estimated a 53.4% reduction (adjusted rate ratio [aRR], 0.47; 95% confidence interval [CI]: .34-.62; P < .01) in malaria incidence and a 30% decrease in the test positivity rate (aRR, 0.70; 95% CI: .50-.97; P = .03) in the intervention village in the 6 months post-intervention. The impact was greatest among children who received the intervention, but decreased incidence was also observed in older children and adults (aRR, 0.57; 95% CI: .38-.84; P < .01). CONCLUSIONS: Three rounds of chemoprevention with DP delivered under pragmatic conditions reduced the incidence of malaria after severe flooding in western Uganda. These findings provide a proof-of-concept for the use of malaria chemoprevention to reduce excess disease burden associated with severe flooding.

Economic optimization of Wolbachia-infected Aedes aegypti release to prevent dengue.

BACKGROUND: Dengue virus, primarily transmitted by the Aedes aegypti mosquito, is a major public health concern affecting ≈3.83 billion people worldwide. Recent releases of Wolbachia-transinfected Ae. aegypti in several cities worldwide have shown that it can reduce dengue transmission. However, these releases are costly, and, to date, no framework has been proposed for determining economically optimal release strategies that account for both costs associated with disease risk and releases. RESULTS: We present a flexible stochastic dynamic programming framework for determining optimal release schedules for Wolbachia-transinfected mosquitoes that balances the cost of dengue infection with the costs of rearing and releasing transinfected mosquitoes. Using an ordinary differential equation model of Wolbachia and dengue in a hypothetical city loosely describing areas at risk of new dengue epidemics, we determined that an all-or-nothing release strategy that quickly brings Wolbachia to fixation is often the optimal solution. Based on this, we examined the optimal facility size, finding that it was inelastic with respect to the mosquito population size, with a 100% increase in population size resulting in a 50-67% increase in optimal facility size. Furthermore, we found that these results are robust to mosquito life-history parameters and are mostly determined by the mosquito population size and the fitness costs associated with Wolbachia. CONCLUSIONS: These results reinforce that Wolbachia-transinfected mosquitoes can reduce the cost of dengue epidemics. Furthermore, they emphasize the importance of determining the size of the target population and fitness costs associated with Wolbachia before releases occur. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction.

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).

Patterns Testing for Tick-Borne Diseases and Implications for Surveillance in the Southeastern US.

Importance: Tick-borne diseases (TBD), including spotted fever group rickettsiosis (SFGR), ehrlichiosis, and, increasingly, Lyme disease, represent a substantial public health concern throughout much of the southeastern United States. Yet, there is uncertainty about the epidemiology of these diseases because of pitfalls in existing diagnostic test methods. Objective: To examine patterns of diagnostic testing and incidence of TBD in a large, academic health care system. Design, Setting, and Participants: This cross-sectional study included diagnostic test results for TBD at UNC Health, a large academic health care system with inpatient and outpatient facilities, from January 1, 2017, to November 30, 2020. Participants included all individuals seeking routine care at UNC Health facilities who had testing for SFGR, ehrlichiosis, or Lyme disease performed during the study period. Main Outcomes and Measures: Rates of test positivity, testing completeness, and incidence of TBD. Results: During the 4-year study period, 11 367 individuals (6633 [58.4%] female; 10 793 [95%] non-Hispanic individuals and 8850 [77.9%] White individuals; median [IQR] age, 53 [37-66] years) were tested for TBD. Among the 20 528 diagnostic tests performed, 47 laboratory-confirmed, incident cases of SFGR, 27 cases of ehrlichiosis, and 76 cases of Lyme were confirmed, representing incidence rates of 4.7%, 7.1%, and 0.7%, respectively. However, 3984 of SFGR tests (79.3%) and 3606 of Ehrlichia tests (74.3%) lacked a paired convalescent sample. Of 20 528 tests, there were 11 977 tests (58.3%) for Lyme disease from 10 208 individuals, 5448 tests (26.5%) for SFGR from 4520 individuals, and 3103 tests (15.1%) for ehrlichiosis from 2507 individuals. Most striking, testing for ehrlichiosis was performed in only 55% of patients in whom SFGR was ordered, suggesting that ehrlichiosis remains underrecognized. An estimated 187 incident cases of SFGR and 309 of ehrlichiosis were potentially unidentified because of incomplete testing. Conclusions and Relevance: In this cross-sectional study, most of the patients suspected of having TBD did not have testing performed in accordance with established guidelines, which substantially limits understanding of TBD epidemiology. Furthermore, the data revealed a large discrepancy between the local burden of disease and the testing performed. These findings underscore the need to pursue more robust, active surveillance strategies to estimate the burden of TBD and distribution of causative pathogens.

Prevalence of Knock-Down Resistance F1534S Mutations in Aedes albopictus (Skuse) (Diptera: Culicidae) in North Carolina.

Knock-down resistance (kdr) mutations in the voltage-gated sodium channel gene of Aedes species mosquitoes are biomarkers for resistance to pyrethroid insecticides. In the United States, few studies have reported kdr mutations among Aedes albopictus (Skuse) (Diptera: Culicidae) populations. In this study, we sought to compare the presence of kdr alleles among Ae. albopictus mosquitoes collected from Fort Bragg and Wake County, North Carolina. We collected 538 Ae. albopictus mosquitoes, including 156 from 4 sites at Fort Bragg, North Carolina and 382 from 15 sites in Wake County, North Carolina to compare the prevalence of kdr mutations. Of those successfully sequenced, we identified 12 (3.0%) mosquitoes with kdr mutations, all of which were attributed to variants at position 1534 within domain 3. All mutations were found in mosquitoes collected at Wake County sites; no mutations were identified in collections from Fort Bragg. There was a focus of mutations observed at the Wake County sites with approximately 92% (11 of 12) of the mosquitoes with the mutation coming from one site, where kdr mutations represented 24.4% (11 of 45) of all mosquitoes collected. We observed highly focal resistance in a suburban area of Raleigh, which may be attributable to peri-domestic mosquito control activities that involve area dispersal of pyrethroid insecticides. More robust surveillance is needed to monitor the emergence and spread of resistance.