RESUMO
When a rare pathogen emerges to cause a pandemic, it is critical to understand its dynamics and the impact of mitigation measures. We use experimental data to parametrize a temperature-dependent model of Zika virus (ZIKV) transmission dynamics and analyse the effects of temperature variability and control-related parameters on the basic reproduction number (R0) and the final epidemic size of ZIKV. Sensitivity analyses show that these two metrics are largely driven by different parameters, with the exception of temperature, which is the dominant driver of epidemic dynamics in the models. Our R0 estimate has a single optimum temperature (≈30°C), comparable to other published results (≈29°C). However, the final epidemic size is maximized across a wider temperature range, from 24 to 36°C. The models indicate that ZIKV is highly sensitive to seasonal temperature variation. For example, although the model predicts that ZIKV transmission cannot occur at a constant temperature below 23°C (≈ average annual temperature of Rio de Janeiro, Brazil), the model predicts substantial epidemics for areas with a mean temperature of 20°C if there is seasonal variation of 10°C (≈ average annual temperature of Tampa, Florida). This suggests that the geographical range of ZIKV is wider than indicated from static R0 models, underscoring the importance of climate dynamics and variation in the context of broader climate change on emerging infectious diseases.
Assuntos
Infecção por Zika virus , Zika virus , Brasil , Florida , Humanos , Mosquitos Vetores , Temperatura , Infecção por Zika virus/epidemiologiaRESUMO
BACKGROUND: Mosquito surveys that collect local data on mosquito species' abundances provide baseline data to help understand potential host-pathogen-mosquito relationships, predict disease transmission, and target mosquito control efforts. METHODS: We conducted an adult mosquito survey from November 2017 to March 2019 on St. Kitts, using Biogents Sentinel 2 traps, set monthly and run for 48-h intervals. We collected mosquitoes from a total of 30 sites distributed across agricultural, mangrove, rainforest, scrub and urban land covers. We investigated spatial variation in mosquito species richness across the island using a hierarchical Bayesian multi-species occupancy model. We developed a mixed effects negative binomial regression model to predict the effects of spatial variation in land cover, and seasonal variation in precipitation on observed counts of the most abundant mosquito species observed. RESULTS: There was high variation among sites in mosquito community structure, and variation in site level richness that correlated with scrub forest, agricultural, and urban land covers. The four most abundant species were Aedes taeniorhynchus, Culex quinquefasciatus, Aedes aegpyti and Deinocerites magnus, and their relative abundance varied with season and land cover. Aedes aegypti was the most commonly occurring mosquito on the island, with a 90% probability of occurring at between 24 and 30 (median = 26) sites. Mangroves yielded the most mosquitoes, with Ae. taeniorhynchus, Cx. quinquefasciatus and De. magnus predominating. Psorophora pygmaea and Toxorhynchites guadeloupensis were only captured in scrub habitat. Capture rates in rainforests were low. Our count models also suggested the extent to which monthly average precipitation influenced counts varied according to species. CONCLUSIONS: There is high seasonality in mosquito abundances, and land cover influences the diversity, distribution, and relative abundance of species on St. Kitts. Further, human-adapted mosquito species (e.g. Ae. aegypti and Cx. quinquefasciatus) that are known vectors for many human relevant pathogens (e.g. chikungunya, dengue and Zika viruses in the case of Ae. aegypti; West Nile, Spondweni, Oropouche virus, and equine encephalitic viruses in the case of Cx. quinqefasciatus) are the most wide-spread (across land covers) and the least responsive to seasonal variation in precipitation.
Assuntos
Distribuição Animal , Culicidae/fisiologia , Ecossistema , Estações do Ano , Aedes/genética , Aedes/fisiologia , Aedes/virologia , Animais , Teorema de Bayes , Culex/genética , Culex/fisiologia , Culex/virologia , Culicidae/classificação , Culicidae/virologia , Controle de Mosquitos , Mosquitos Vetores/fisiologia , Mosquitos Vetores/virologia , São Cristóvão e NévisRESUMO
BACKGROUND: Yellow fever virus is a mosquito-borne flavivirus that persists in an enzoonotic cycle in non-human primates (NHPs) in Brazil, causing disease in humans through spillover events. Yellow fever (YF) re-emerged in the early 2000s, spreading from the Amazon River basin towards the previously considered low-risk, southeastern region of the country. Previous methods mapping YF spillover risk do not incorporate the temporal dynamics and ecological context of the disease, and are therefore unable to predict seasonality in spatial risk across Brazil. We present the results of a bagged logistic regression predicting the propensity for YF spillover per municipality (administrative sub-district) in Brazil from environmental and demographic covariates aggregated by month. Ecological context was incorporated by creating National and Regional models of spillover dynamics, where the Regional model consisted of two separate models determined by the regions' NHP reservoir species richness (high vs low). RESULTS: Of the 5560 municipalities, 82 reported YF cases from 2001 to 2013. Model accuracy was high for the National and low reservoir richness (LRR) models (AUC = 0.80), while the high reservoir richness (HRR) model accuracy was lower (AUC = 0.63). The National model predicted consistently high spillover risk in the Amazon, while the Regional model predicted strong seasonality in spillover risk. Within the Regional model, seasonality of spillover risk in the HRR region was asynchronous to the LRR region. However, the observed seasonality of spillover risk in the LRR Regional model mirrored the national model predictions. CONCLUSIONS: The predicted risk of YF spillover varies with space and time. Seasonal trends differ between regions indicating, at times, spillover risk can be higher in the urban coastal regions than the Amazon River basin which is counterintuitive based on current YF risk maps. Understanding the spatio-temporal patterns of YF spillover risk could better inform allocation of public health services.