Joseph Biggs

Background: Reports of arboviral outbreaks are increasing, yet there are acknowledged shortfalls in surveillance data and outbreak preparedness on the African continent. The second part of this scoping review features Aedes vector–pathogen interactions, bionomics and species distribution. Methods: We reviewed studies published between 1980 and 2023 to synthesize existing information and identify knowledge gaps to guide future Aedes research and control in Africa. Results: 877 full-text articles were reviewed, 70 featured vector–pathogen interactions, 230 vector bionomics and 394 species distribution. Vector competence studies were limited to a handful of countries, highlighting knowledge gaps regarding urban, bridge and sylvatic vector species competence for major arboviruses. Aedes albopictus was highly competent for chikungunya virus with the E1-226V mutation, which has been implicated in epidemics where Ae. aegypti was displaced. Vector competence was dependent on virus lineage and mosquito population. Ae. aegypti competence to dengue virus was highly variable, whereas Ae. albopictus was an efficient vector of Zika virus for African but not Asian lineages. Discarded tires were the most frequently reported positive breeding habitats, ranking among the top three in 55% of publications, followed by other discarded items (cans, bottles, broken plastic items) and water storage containers; however, the relative importance varied by location. The pupal index was reported less frequently than traditional indices (container, Breteau and house indices), which co-occurred in 84% of studies reporting indices. As pupal counts are a better proxy for adult mosquito production, their wider use is recommended to identify locally important breeding sites for targeted control. Adult vector monitoring has shifted away from human landing catch towards BG-Sentinel and CDC-LT traps which may be less accurate proxies of human-vector contact, but efficiency can be improved using attractants. Ae. aegypti was highly anthropophilic and had a major biting peak before sunset. However, findings of Ae. aegypti biting at night-time and being zoophilic in some locations demonstrate the need for periodic monitoring, given changing environmental conditions. Conclusions: This review highlights how Aedes populations vary in their competence for key arboviruses, their breeding habitats, and biting behaviors across Africa. Understanding these patterns can guide targeted vector control and improve preparedness for arbovirus outbreaks.

Climate change is accelerating the geographical expansion of Aedes mosquito vectors, facilitating the emergence of arboviral diseases such as dengue in new regions. However, there is limited understanding of how environmental factors, such as temperature and population density, differentially influence key metrics of dengue transmission. This study investigates how elevation (a proxy for temperature) and population density affect dengue incidence and force of infection (FOI), both independently and dependently, across the Philippines, with implications for climate-adapted surveillance and control. We conducted a nationwide, cross-sectional survey across the Philippines (2013–2019), combining national dengue case data (N = 1,112,317) with antibody IgG seroprevalence data from a representative sample (N = 22,270). Dengue FOI (the predicted rate at which individuals become exposed to DENV annually) and incidence were estimated across elevation and population density strata using catalytic models. Regression analyses were used to assess interactions between geographic factors and the dengue FOI. Elevation and population density were associated with FOI, but not with reported dengue incidence, which varied unpredictably over space and time. Urban, low-elevation barangays had the highest FOI (13.2%), while rural and urban, high-elevation areas had lower FOI (force of infection—the underlying rate at which people are exposed to dengue, including infections without noticeable symptoms. Reported dengue incidence varied unpredictably across regions and years and showed no consistent relationship with elevation or population density. In contrast, force of infection revealed clear and stable patterns. Transmission was highest in low-lying, densely populated urban areas and substantially lower at higher elevations, regardless of population density. Population density increased transmission only in low-elevation areas, where warmer temperatures favour mosquito survival. These findings show that commonly used epidemiological metrics including case counts and incidence, based on reported cases, can misrepresent dengue risk. Measures that capture underlying transmission provide more reliable guidance for surveillance and control. As climate change warms higher-elevation regions, geographically targeted strategies will be essential to prevent future dengue outbreaks.

Cluster randomised trials (CRTs) are important tools for evaluating the community-wide effect of malaria interventions. During the design stage, CRT sample sizes need to be inflated to account for the cluster heterogeneity in measured outcomes. The coefficient of variation (k), a measure of such heterogeneity, is typically used in malaria CRTs yet is often predicted without prior data. Underestimation of k decreases study power, thus increases the probability of generating null results. In this meta-analysis of cluster-summary data from 24 malaria CRTs, we calculate true prevalence and incidence k values using methods-of-moments and regression modelling approaches. Using random effects regression modelling, we investigate the impact of empirical k values on original trial power and explore factors associated with elevated k. Results show empirical estimates of k often exceed those used in sample size calculations, which reduces study power and effect size precision. Elevated k values are associated with incidence outcomes (compared to prevalence), lower endemicity settings, and uneven intervention coverage across clusters. Study findings can enhance the robustness of future malaria CRT sample size calculations by providing informed k estimates based on expected prevalence or incidence, in the absence of cluster-level data.

Cluster randomised trials (CRTs) are important tools for evaluating the community-wide effect of malaria interventions. During the design stage, CRT sample sizes need to be inflated to account for the cluster-heterogeneity in measured outcomes. One such measure of heterogeneity, the coefficient of variation (k), is typically used in malaria CRTs yet is often estimated without prior data. Underestimation of k undermines study power and increases the probability of CRTs generating null results. We conducted a meta-analysis of cluster-summary data from 24 malaria CRTs and calculated true k values for prevalence and incidence outcomes using methods-of-moments and regression modelling approaches. Using random effects regression modelling we investigated the impact of empirical k values on original trial power, effect size uncertainty and explored associated factors. Results revealed empirical estimates of k often exceeded those used in sample size calculations which heavily contributed to compromised study power and effect size precision. Increased between-cluster heterogeneity of outcomes was associated with outcome measures (i.e. incidence or prevalence), lower endemicity, seasonality of surveys and uneven intervention coverage across clusters. Study findings can be used to inform future malaria CRT sample size calculations and trial design to help ensure malaria interventions are effectively and feasibly evaluated.

Introduction: Cluster randomised trials (CRTs) are the gold standard for measuring the community-wide impacts of malaria control tools. CRTs rely on well-defined sample size estimations to detect statistically significant effects of trialled interventions, however these are often predicted poorly by triallists. Here, we review the accuracy of predicted parameters used in sample size calculations for malaria CRTs with epidemiological outcomes. Methods: We searched for published malaria CRTs using four online databases in March 2022. Eligible trials included those with malaria-specific epidemiological outcomes which randomised at least six geographical clusters to study arms. Predicted and observed sample size parameters were extracted by reviewers for each trial. Pair-wise Spearman’s correlation coefficients (r_s) were calculated to assess the correlation between predicted and observed control-arm outcome measures and effect sizes (relative percentage reductions) between arms. Among trials which retrospectively calculated an estimate of heterogeneity in cluster outcomes, we recalculated study power according to observed trial estimates. Results: Of the 1889 records identified and screened, 108 articles were eligible and comprised of 71 malaria CRTs. Among 91.5% (65/71) of trials that included sample size calculations, most estimated cluster heterogeneity using the coefficient of variation (k) (80%, 52/65) which were often predicted without using prior data (67.7%, 44/65). Predicted control-arm prevalence moderately correlated with observed control-arm prevalence (r_s: 0.44, [95%CI: 0.12,0.68], p-value < 0.05], with 61.2% (19/31) of prevalence estimates overestimated. Among the minority of trials that retrospectively calculated cluster heterogeneity (20%, 13/65), empirical values contrasted with those used in sample size estimations and often compromised study power. Observed effect sizes were often smaller than had been predicted at the sample size stage (72.9%, 51/70) and were typically higher in the first, compared to the second, year of trials. Overall, effect sizes achieved by malaria interventions tested in trials decreased between 1995 and 2021. Conclusions: Study findings reveal sample size parameters in malaria CRTs were often inaccurate and resulted in underpowered studies. Future trials must strive to obtain more representative epidemiological sample size inputs to ensure interventions against malaria are adequately evaluated. Registration: This review is registered with PROSPERO (CRD42022315741).

Introduction Cluster randomised trials (CRTs) are the gold standard for measuring the community-wide impacts of malaria control tools. CRTs rely on well-defined sample size estimations to detect statistically significant effects of trialled interventions; however these are often predicted poorly by investigators. Here, we review the accuracy of predicted parameters used in sample size calculations for malaria CRTs with epidemiological outcomes. Methods We searched for published malaria CRTs using four online databases in March 2022. Eligible trials included those with malaria-specific epidemiological outcomes which randomised at least six geographical clusters to study arms. Predicted and observed sample size parameters were extracted by reviewers for each trial. Pair-wise correlation coefficients were calculated to assess the accuracy of predicted control-arm outcome estimates and desired relative reductions (effect sizes) between arms compared to what was observed. Among trials which retrospectively calculated an estimate of heterogeneity in cluster outcomes, we recalculated study power according to observed trial estimates. Results Of the 1889 records identified and screened, 108 articles were eligible and comprised of 71 malaria CRTs. Among trials that included sample size calculations (91.5%, 65/71), most estimated cluster heterogeneity using the coefficient of variation (k) (80%, 52/65) which were often predicted without using prior data (67.7%, 44/65). Predicted control-arm epidemiological outcomes correlated weakly with those observed, with 61.2% (19/31) of prevalence estimates overestimated. Among the minority of trials which retrospectively calculated cluster heterogeneity (20%, 13/65), empirical values contrasted with those used in sample size estimations and often compromised study power. Observed effect sizes were often smaller than had been predicted at the sample size stage (72.9%, 51/70) and were typically higher in the first, compared to the second, year of trials. Overall, effect sizes achieved by malaria interventions tested in trials decreased between 1995 and 2021. Conclusions Study findings reveal sample size parameters in malaria CRTs were often inaccurate and resulted in underpowered studies. Future trials must strive to obtain more representative epidemiological sample size inputs to ensure interventions against malaria are adequately evaluated. Registration This review is registered with PROSPERO (CRD42022315741).

Background: Characterising dengue virus (DENV) infection history at the point of care is challenging as it relies on intensive laboratory techniques. We investigated how combining different rapid diagnostic tests (RDTs) can be used to accurately determine the primary and post-primary DENV immune status of reporting patients during diagnosis. Methods and findings: Serum from cross-sectional surveys of acute suspected dengue patients in Indonesia (N:200) and Vietnam (N: 1,217) were assayed using dengue laboratory assays and RDTs. Using logistic regression modelling, we determined the probability of being DENV NS1, IgM and IgG RDT positive according to corresponding laboratory viremia, IgM and IgG ELISA metrics. Laboratory test thresholds for RDT positivity/negativity were calculated using Youden’s J index and were utilized to estimate the RDT outcomes in patients from the Philippines, where only data for viremia, IgM and IgG were available (N:28,326). Lastly, the probabilities of being primary or post-primary according to every outcome using all RDTs, by day of fever, were calculated. Combining NS1, IgM and IgG RDTs captured 94.6% (52/55) and 95.4% (104/109) of laboratory-confirmed primary and post-primary DENV cases, respectively, during the first 5 days of fever. Laboratory test predicted, and actual, RDT outcomes had high agreement (79.5% (159/200)). Among patients from the Philippines, different combinations of estimated RDT outcomes were indicative of post-primary and primary immune status. Overall, IgG RDT positive results were confirmatory of post-primary infections. In contrast, IgG RDT negative results were suggestive of both primary and post-primary infections on days 1–2 of fever, yet were confirmatory of primary infections on days 3–5 of fever. Conclusion: We demonstrate how the primary and post-primary DENV immune status of reporting patients can be estimated at the point of care by combining NS1, IgM and IgG RDTs and considering the days since symptoms onset. This framework has the potential to strengthen surveillance operations and dengue prognosis, particularly in low resource settings.

Background: Stratifying dengue risk within endemic countries is crucial for allocating limited control interventions. Current methods of monitoring dengue transmission intensity rely on potentially inaccurate incidence estimates. We investigated whether incidence or alternate metrics obtained from standard, or laboratory, surveillance operations represent accurate surrogate indicators of the burden of dengue and can be used to monitor the force of infection (FOI) across urban centres. Methods: Among those who reported and resided in 13 cities across the Philippines, we collected epidemiological data from all dengue case reports between 2014 and 2017 (N 80,043) and additional laboratory data from a cross-section of sampled case reports (N 11,906) between 2014 and 2018. At the city level, we estimated the aggregated annual FOI from age-accumulated IgG among the non-dengue reporting population using catalytic modelling. We compared city-aggregated FOI estimates to aggregated incidence and the mean age of clinically and laboratory diagnosed dengue cases using Pearson’s Correlation coefficient and generated predicted FOI estimates using regression modelling. Results: We observed spatial heterogeneity in the dengue average annual FOI across sampled cities, ranging from 0.054 [0.036–0.081] to 0.249 [0.223–0.279]. Compared to FOI estimates, the mean age of primary dengue infections had the strongest association (ρ −0.848, p valueρ −0.642, p value 0.018). Using regression modelling, we estimated the predicted annual dengue FOI across urban centres from the age of those reporting with primary infections and revealed prominent spatio-temporal heterogeneity in transmission intensity. Conclusions: We show the mean age of those reporting with their first dengue infection or those reporting with warning signs of dengue represent superior indicators of the dengue FOI compared to crude incidence across urban centres. Our work provides a framework for national dengue surveillance to routinely monitor transmission and target control interventions to populations most in need.

Zika virus (ZIKV) exposure across flavivirus-endemic countries, including the Philippines, remains largely unknown despite sporadic case reporting and environmental suitability for transmission. Using laboratory surveillance data from 2016, 997 serum samples were randomly selected from suspected dengue (DENV) case reports across the Philippines and assayed for serological markers of short-term (IgM) and long-term (IgG) ZIKV exposure. Using mixture models, we re-evaluated ZIKV IgM/G seroprevalence thresholds and used catalytic models to quantify the force of infection (attack rate, AR) from age-accumulated ZIKV exposure. While we observed extensive ZIKV/DENV IgG cross-reactivity, not all individuals with active DENV presented with elevated ZIKV IgG, and a proportion of dengue-negative cases (DENV IgG-) were ZIKV IgG-positive (14.3%, 9/63). We identified evidence of long-term, yet not short-term, ZIKV exposure across Philippine regions (ZIKV IgG+: 31.5%, 314/997) which was geographically uncorrelated with DENV exposure. In contrast to the DENV AR (12.7% (95%CI: 9.1–17.4%)), the ZIKV AR was lower (5.7% (95%CI: 3–11%)) across the country. Our results provide evidence of widespread ZIKV exposure across the Philippines and suggest the need for studies to identify ZIKV infection risk factors over time to better prepare for potential future outbreaks.

Background: In dengue-endemic countries, targeting limited control interventions to populations at risk of severe disease could enable increased efficiency. Individuals who have had their first (primary) dengue infection are at risk of developing more severe secondary disease, thus could be targeted for disease prevention. Currently, there is no reliable algorithm for determining primary and post-primary (infection with more than one flavivirus) status from a single serum sample. In this study, we developed and validated an immune status algorithm using single acute serum samples from reporting patients and investigated dengue immuno-epidemiological patterns across the Philippines. Methods: During 2015/2016, a cross-sectional sample of 10,137 dengue case reports provided serum for molecular (anti-DENV PCR) and serological (anti-DENV IgM/G capture ELISA) assay. Using mixture modelling, we re-assessed IgM/G seroprevalence and estimated functional, disease day-specific, IgG:IgM ratios that categorised the reporting population as negative, historical, primary and post-primary for dengue. We validated our algorithm against WHO gold standard criteria and investigated cross-reactivity with Zika by assaying a random subset for anti-ZIKV IgM and IgG. Lastly, using our algorithm, we explored immuno-epidemiological patterns of dengue across the Philippines. Results: Our modelled IgM and IgG seroprevalence thresholds were lower than kit-provided thresholds. Individuals anti-DENV PCR+ or IgM+ were classified as active dengue infections (83.1%, 6998/8425). IgG− and IgG+ active dengue infections on disease days 1 and 2 were categorised as primary and post-primary, respectively, while those on disease days 3 to 5 with IgG:IgM ratios below and above 0.45 were classified as primary and post-primary, respectively. A significant proportion of post-primary dengue infections had elevated anti-ZIKV IgG inferring previous Zika exposure. Our algorithm achieved 90.5% serological agreement with WHO standard practice. Post-primary dengue infections were more likely to be older and present with severe symptoms. Finally, we identified a spatio-temporal cluster of primary dengue case reporting in northern Luzon during 2016. Conclusions: Our dengue immune status algorithm can equip surveillance operations with the means to target dengue control efforts. The algorithm accurately identified primary dengue infections who are at risk of future severe disease.