Anna B. Kawiecki

Dengue transmission is highly sensitive to climate variability, with increasing evidence that cascading and compound extreme events increase outbreak risk. In Brazil and Barbados, drought conditions at long temporal lags (4–6 months) followed by extreme wet conditions at short lags (1–3 months) have been associated with increased dengue risk, particularly when combined with elevated temperatures (Fletcher et al. 2025; Lowe et al. 2021). In Colombia, dengue incidence has similarly been linked to El Niño–Southern Oscillation (ENSO)–driven increases in temperature and reductions in precipitation (Muñoz et al. 2021), as well as to long-lag drought effects that vary across altitudinal and urbanization gradients (Kache et al. 2024; Lowe et al. 2021). Despite this growing body of evidence, the extent to which anthropogenic climate change has altered dengue risk through changes in extreme event patterns remains largely unquantified. Here, we develop a predictive modelling framework that integrates the interacting effects of multiple climate extremes (drought, extreme rainfall, heatwaves) at short and long temporal lags to estimate dengue case incidence in Colombia under current climate conditions and under counterfactual pre-industrial climate conditions, thereby quantifying changes in dengue outbreak probability attributable to anthropogenic climate change. Multiple realizations of climate models simulating precipitation and temperature extremes under current and pre-industrial climate conditions will be used as inputs to the dengue risk model, allowing estimation of dengue outbreak probability attributable to climate change while explicitly characterizing uncertainty arising from both climate and epidemiological model components. This attribution framework will provide a transferable approach for quantifying climate-sensitive infectious disease outbreak risk attributable to anthropogenic climate change.

Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the 'INLA' framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) .

A collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.

Background: Aedes aegypti is the primary mosquito vector for several arboviruses, such as dengue, chikungunya and Zika viruses, which cause frequent outbreaks of human disease in tropical and subtropical regions. Control of these outbreaks relies on vector control, commonly in the form of insecticide sprays that target adult female mosquitoes. However, the spatial coverage and frequency of sprays needed to optimize effectiveness are unclear. In this study, we characterize the effect of ultra-low-volume (ULV) indoor spraying of pyrethroid insecticides on Ae. aegypti abundance within households. We also evaluate the effects of spray events during recent time periods or in neighboring households. Improved understanding of the duration and distance of the impact of a spray intervention on Ae. aegypti populations can inform vector control interventions, in addition to modeling efforts that contrast vector control strategies. Methods: This project analyzes data from two large-scale experiments that involved six cycles of indoor pyrethroid spray applications in 2 years in the Amazonian city of Iquitos, Peru. We developed spatial multi-level models to disentangle the reduction in Ae. aegypti abundance that resulted from (i) recent ULV treatment within households and (ii) ULV treatment of adjacent or nearby households. We compared fits of models across a range of candidate weighting schemes for the spray effect, based on different temporal and spatial decay functions to understand lagged ULV effects. Results: Our results suggested that the reduction of Ae. aegypti in a household was mainly due to spray events occurring within the same household, with no additional effect of sprays that occurred in neighboring households. Effectiveness of a spray intervention should be measured based on time since the most recent spray event, as we found no cumulative effect of sequential sprays. Based on our model, we estimated the spray effect is reduced by 50% approximately 28 days after the spray event. Conclusions: The reduction of Ae. aegypti in a household was mainly determined by the number of days since the last spray intervention in that same household, highlighting the importance of spray coverage in high-risk areas with a spray frequency determined by local viral transmission dynamics. Graphical

Current knowledge of dengue virus (DENV) transmission provides only a partial understanding of a complex and dynamic system yielding a public health track record that has more failures than successes. An important part of the problem is that the foundation for contemporary interventions includes a series of longstanding, but untested, assumptions based on a relatively small portion of the human population; i.e., people who are convenient to study because they manifest clinically apparent disease. Approaching dengue from the perspective of people with overt illness has produced an extensive body of useful literature. It has not, however, fully embraced heterogeneities in virus transmission dynamics that are increasingly recognized as key information still missing in the struggle to control the most important insect-transmitted viral infection of humans. Only in the last 20 years have there been significant efforts to carry out comprehensive longitudinal dengue studies. This manuscript provides the rationale and comprehensive, integrated description of the methodology for a five-year longitudinal cohort study based in the tropical city of Iquitos, in the heart of the Peruvian Amazon. Primary data collection for this study was completed in 2019. Although some manuscripts have been published to date, our principal objective here is to support subsequent publications by describing in detail the structure, methodology, and significance of a specific research program. Our project was designed to study people across the entire continuum of disease, with the ultimate goal of quantifying heterogeneities in human variables that affect DENV transmission dynamics and prevention. Because our study design is applicable to other Aedes transmitted viruses, we used it to gain insights into Zika virus (ZIKV) transmission when during the project period ZIKV was introduced and circulated in Iquitos. Our prospective contact cluster investigation design was initiated by detecttion of a person with a symptomatic DENV infection and then followed that person’s immediate contacts. This allowed us to monitor individuals at high risk of DENV infection, including people with clinically inapparent and mild infections that are otherwise difficult to detect. We aimed to fill knowledge gaps by defining the contribution to DENV transmission dynamics of (1) the understudied majority of DENV-infected people with inapparent and mild infections and (2) epidemiological, entomological, and socio-behavioral sources of heterogeneity. By accounting for factors underlying variation in each person’s contribution to transmission we sought to better determine the type and extent of effort needed to better prevent virus transmission and disease.

Vector-borne diseases are among the most burdensome infectious diseases worldwide with high burden to health systems in developing regions in the tropics. For many of these diseases, vector control to reduce human biting rates or arthropod populations remains the primary strategy for prevention. New vector control interventions intended to be marketed through public health channels must be assessed by the World Health Organization for public health value using data generated from large-scale trials integrating epidemiological endpoints of human health impact. Such phase III trials typically follow large numbers of study subjects to meet necessary power requirements for detecting significant differences between treatment arms, thereby generating substantive and complex datasets. Data is often gathered directly in the field, in resource-poor settings, leading to challenges in efficient data reporting and/or quality assurance. With advancing technology, mobile data collection (MDC) systems have been implemented in many studies to overcome these challenges. Here we describe the development and implementation of a MDC system during a randomized-cluster, placebo-controlled clinical trial evaluating the protective efficacy of a spatial repellent intervention in reducing human infection with Aedes-borne viruses (ABV) in the urban setting of Iquitos, Peru, as well as the data management system that supported it. We discuss the benefits, remaining capacity gaps and the key lessons learned from using a MDC system in this context in detail.

Over half the world’s population is at risk for viruses transmitted by Aedes mosquitoes, such as dengue and Zika. The primary vector, Aedes aegypti , thrives in urban environments. Despite decades of effort, cases and geographic range of Aedes -borne viruses (ABVs) continue to expand. Rigorously proven vector control interventions that measure protective efficacy against ABV diseases are limited to Wolbachia in a single trial in Indonesia and do not include any chemical intervention. Spatial repellents, a new option for efficient deployment, are designed to decrease human exposure to ABVs by releasing active ingredients into the air that disrupt mosquito–human contact. A parallel, cluster-randomized controlled trial was conducted in Iquitos, Peru, to quantify the impact of a transfluthrin-based spatial repellent on human ABV infection. From 2,907 households across 26 clusters (13 per arm), 1,578 participants were assessed for seroconversion (primary endpoint) by survival analysis. Incidence of acute disease was calculated among 16,683 participants (secondary endpoint). Adult mosquito collections were conducted to compare Ae. aegypti abundance, blood-fed rate, and parity status through mixed-effect difference-in-difference analyses. The spatial repellent significantly reduced ABV infection by 34.1% (one-sided 95% CI lower limit, 6.9%; one-sided P value = 0.0236, z = 1.98). Aedes aegypti abundance and blood-fed rates were significantly reduced by 28.6 (95% CI 24.1%, ∞); z = −9.11) and 12.4% (95% CI 4.2%, ∞); z = −2.43), respectively. Our trial provides conclusive statistical evidence from an appropriately powered, preplanned cluster-randomized controlled clinical trial of the impact of a chemical intervention, in this case a spatial repellent, to reduce the risk of ABV transmission compared to a placebo.

Vectorborne diseases are a growing threat to global public health, causing a high burden of disease and mortality, especially in developing nations in the tropics (Bhatt et al. 2013). The Aedes aegypti mosquito is the primary vector for several arboviruses that can cause severe epidemics, such as dengue, Zika and chikungunya, and is especially concerning due to the expansion of its geographic range and adaptation to urban environments. Control and prevention of Aedes-transmitted diseases rely on vector control strategies, however there is insufficient understanding of the efficacy of any given strategy on vector density and disease reduction in natural field conditions (Wilson et al. 2015). In addition, there are few studies that investigate the effects of vector control interventions on the vector populations in neighboring areas not covered by the intervention, or how these effects change over space and time. Ultra-low Volume (ULV) indoor insecticide spraying has been shown to be effective at reducing Ae. aegypti density for short periods of time (approximately two weeks post-intervention; Gunning et al. 2018). Unsprayed areas near the spray zone may see a reduction in the Ae. aegypti population as well, however the spatial extent of the vector control effect beyond the spray zone is unclear, as is the effect of incomplete spray coverage within the intervention area. The effect may be limited by the flight range of Ae. aegypti mosquitoes, which generally cluster within 10-30 m of their source household (Getis et al. 2010). However, these behaviors may change in the presence of vector control interventions. In this study, we characterize the impact of ULV indoor spraying on the A. aegypti population within households in relation to the spray events occurring in the proximity of that household in time and space. We will use data from two large-scale experiments that involved six cycles of indoor pyrethroid spray applications in two distinct neighborhoods of the Amazonian city of Iquitos, Peru. These experiments divided the study area into a central “spray zone” that received the intervention, surrounded by a “buffer zone” that did not. Adult mosquitoes were collected by hand aspirators across the entire study area before, during and after the intervention. Two candidate models will be compared for each experiment: (1) at the household level, a general linear mixed model will estimate the Ae. aegypti count per household by including a fixed effect for the intervention on the household weighted by the distance in time and space of nearby houses that received the intervention, thereby adding spatiotemporal effects to the intervention variable; (2) at the zone level, a negative binomial regression model will estimate the spillover of the intervention effect into unsprayed neighboring houses by estimating the relationship between the number of Ae. aegypti in houses in the buffer zone and the distance in space and time to the spray zone. With this analysis we provide evidence for the spatial coverage necessary to achieve vector reductions as well as the impact of ULV indoor spraying on neighboring vector populations outside the intervention area. This knowledge can be used to inform targeted and more cost-effective vector control programs, decreasing the economic burden caused by arboviral diseases in developing regions.