Systematic Review of Impacts of Educational Interventions to Control Breeding Sites of Aedes aegypti and Aedes albopictus Mosquitoes.

Community participation is a critical element in the management of Aedes aegypti and Aedes albopictus breeding sites. Many educational interventions have been conducted to encourage prevention and elimination of breeding sites among different community actors, such as government-run programs for vector surveillance aimed at preventing and eliminating breeding sites at the household level within a community. Getting people involved in prevention and elimination of vector breeding sites in their communities requires communication and social mobilization strategies to promote and reinforce those prevention actions that, in turn, should be effective from the entomological standpoint. Articles published in English, Spanish, and Portuguese, were reviewed to assess whether educational interventions targeting Ae. aegypti and Ae. albopictus were effective in reducing entomological indicators or in improving practices to prevent the presence of or eliminate breeding sites. The most widely used indicators were larval indices and the practices associated with reducing/eliminating breeding sites. We found that using a community-based approach adapted to eco-epidemiological and sociocultural scenarios explains the reduction of entomological indicators by educational interventions. Those who design or implement educational interventions should strengthen the evaluation of those interventions using qualitative approaches that provide a more complete picture of the social context and the barriers and facilitators to implementing vector control. Engaging school children in cross-sectorial collaboration involving the health and education spheres promotes the participation of the community in vector surveillance and reduces the risk of arboviral disease transmission.

High prevalence of Zika virus infection in populations of Aedes aegypti from South-western Ecuador.

We performed an arboviral survey in mosquitoes from four endemic Ecuadorian cities (Huaquillas, Machala, Portovelo and Zaruma) during the epidemic period 2016-2018. Collections were performed during the pre-rainy season (2016), peak transmission season (2017) and post-rainy season (2018). Ae. aegypti mosquitoes were pooled by date, location and sex. Pools were screened by RT-PCR for the presence of ZIKV RNA, and infection rates (IRs) per 1,000 specimens were calculated. A total of 2,592 pools (comprising 6,197 mosquitoes) were screened. Our results reveal high IRs in all cities and periods sampled. Overall IRs among female mosquitoes were highest in Machala (89.2), followed by Portovelo (66.4), Zaruma (47.4) and Huaquillas (41.9). Among male mosquitoes, overall IRs were highest in Machala (35.6), followed by Portovelo (33.1), Huaquillas (31.9) and Zaruma (27.9), suggesting that alternative transmission routes (vertical/venereal) can play important roles for ZIKV maintenance in the vector population of these areas. Additionally, we propose that the stabilization of ZIKV vertical transmission in the vector population could help explain the presence of high IRs in field-caught mosquitoes during inter-epidemic periods.

PHENOMENOLOGICAL FORECASTING OF DISEASE INCIDENCE USING HETEROSKEDASTIC GAUSSIAN PROCESSES: A DENGUE CASE STUDY.

In 2015 the US federal government sponsored a dengue forecasting competition using historical case data from Iquitos, Peru and San Juan, Puerto Rico. Competitors were evaluated on several aspects of out-of-sample forecasts including the targets of peak week, peak incidence during that week, and total season incidence across each of several seasons. our team was one of the winners of that competition, outperforming other teams in multiple targets/locales. In this paper we report on our methodology, a large component of which, surprisingly, ignores the known biology of epidemics at large-for example, relationships between dengue transmission and environmental factors-and instead relies on flexible nonparametric nonlinear Gaussian process (GP) regression fits that "memorize" the trajectories of past seasons, and then "match" the dynamics of the unfolding season to past ones in real-time. Our phenomenological approach has advantages in situations where disease dynamics are less well understood, or where measurements and forecasts of ancillary covariates like precipitation are unavailable, and/or where the strength of association with cases are as yet unknown. In particular, we show that the GP approach generally outperforms a more classical generalized linear (autoregressive) model (GLM) that we developed to utilize abundant covariate information. We illustrate variations of our method(s) on the two benchmark locales alongside a full summary of results submitted by other contest competitors.