Forecasting Chikungunya spread in the Americas via data-driven empirical approaches

Temporal dynamics of the egg bank of Aedes aegypti (Diptera: Culicidae) in the winter-spring transition in a temperate region

In temperate regions, the populations of Aedes aegypti (L.) (Diptera: Culicidae) remain in the egg stage during the winter. In Buenos Aires, Argentina, a temperate region in the southern limit of Ae. aegypti distribution, the start of the next reproductive season and the rate of increase of the adult population depend on the egg bank that remains after the winter. This study aimed to analyze the mortality, field-hatching, and survival of eggs exposed to field conditions representative of those that occur in nature. In addition, the post-exposure hatching response of the eggs was assessed. Four egg batches were exposed to natural conditions starting in mid-winter and were recovered progressively after 3, 6, 9, and 12 weeks. One egg batch (initial control) was not exposed in the field and remained under laboratory conditions. After the exposure period, the recovered intact, collapsed, and hatched eggs were counted. Intact eggs were immersed three times in the laboratory to study their hatching response. Progressive increases in the proportion of lost (presumably by predation), dead, and hatched eggs in successive egg batches were recorded. Field-hatching was recorded from late winter onwards. The first hatchings occurred in conditions probably not favorable to complete development into reproductive adults. A progressive decrease in live eggs was observed, with 51% of the recovered eggs alive after 12 weeks of exposure. In the laboratory, the hatching response in the first immersion was low for the initial control and for the eggs exposed for 3 weeks, and increased for successive cohorts. The results confirm that the survival of Ae. aegypti eggs in the winter-spring transition ensures persistence throughout the next favorable season in Buenos Aires City. The observed inhibition to hatch of the first batches might relate to a photoperiod-induced diapause, as observed in previous studies.

Global Mpox spread due to increased air travel

Mpox is an emerging, infectious disease that has caused outbreaks in at least 91 countries from May to August 2022. We assessed the link between international air travel patterns and Mpox transmission risk, and the relationship between the translocation of Mpox and human mobility dynamics after travel restrictions due to the COVID-19 pandemic had been lifted. Our three novel observations were that: i) more people traveled internationally after the removal of travel restrictions in the summer of 2022 compared to pre-pandemic levels; ii) countries with a high concentration of global air travel have the most recorded Mpox cases; and iii) Mpox transmission includes a number of previously nonendemic regions. These results suggest that international airports should be a primary location for monitoring the risk of emerging communicable diseases. Findings highlight the need for global collaboration concerning proactive measures emphasizing realtime surveillance.

Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability

Background

Aedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens. Many recent dengue fever outbreaks in China have been caused solely by the vector. Mapping of the potential distribution ranges of Ae. albopictus is crucial for epidemic preparedness and the monitoring of vector populations for disease control. Climate is a key factor influencing the distribution of the species. Despite field studies indicating seasonal population variations, very little modeling work has been done to analyze how environmental conditions influence the seasonality of Ae. albopictus. The aim of the present study was to develop a model based on available observations, climatic and environmental data, and machine learning methods for the prediction of the potential seasonal ranges of Ae. albopictus in China.

Methods

We collected comprehensive up-to-date surveillance data in China, particularly records from the northern distribution margin of Ae. albopictus. All records were assigned long-term (1970–2000) climatic data averages based on the WorldClim 2.0 data set. Machine learning regression tree models were developed using a 10-fold cross-validation method to predict the potential seasonal (or monthly) distribution ranges of Ae. albopictus in China at high resolution based on environmental conditions. The models were assessed based on sensitivity, specificity, and accuracy, using area under curve (AUC). WorldClim 2.0 and climatic and environmental data were used to produce environmental conduciveness (probability) prediction surfaces. Predicted probabilities were generated based on the averages of the 10 models.

Results

During 1998–2017, Ae. albopictus was observed at 200 out of the 242 localities surveyed. In addition, at least 15 new Ae. albopictus occurrence sites lay outside the potential ranges that have been predicted using models previously. The average accuracy was 98.4% (97.1–99.5%), and the average AUC was 99.1% (95.6–99.9%). The predicted Ae. albopictus distribution in winter (December–February) was limited to a small subtropical-tropical area of China, and Ae. albopictus was predicted to occur in northern China only during the short summer season (usually June–September). The predicted distribution areas in summer could reach northeastern China bordering Russia and the eastern part of the Qinghai-Tibet Plateau in southwestern China. Ae. albopictus could remain active in expansive areas from central to southern China in October and November.

Conclusions

Climate and environmental conditions are key factors influencing the seasonal distribution of Ae. albopictus in China. The areas predicted to potentially host Ae. albopictus seasonally in the present study could reach northeastern China and the eastern slope of the Qinghai-Tibet Plateau. Our results present new evidence and suggest the expansion of systematic vector population monitoring activities and regular re-assessment of epidemic risk potential.

Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R₀. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity

BACKGROUND

Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data.

METHODS

We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data.

RESULTS

We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting.

CONCLUSIONS

Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.

Effects of scarcity and excess of larval food on life history traits of Aedes aegypti (Diptera: Culicidae)

Few studies have assessed the effects of food scarcity or excess on the life history traits of Aedes aegypti (L.) (Diptera: Culicidae) independently from larval density. We assessed immature survival, development time, and adult size in relation to food availability. We reared cohorts of 30 Ae. aegypti larvae from newly hatched to adult emergence with different food availability. Food conditions were kept constant by transferring larvae each day to a new food solution. Immature development was completed by some individuals in all treatments. The shortest development time, the largest adults, and the highest survival were observed at intermediate food levels. The most important effects of food scarcity were an extension in development time, a decrease in the size of adults, and a slight decrease in survival, while the most important effects of food excess were an important decrease in survival and a slight decrease in the size of adults. The variability in development time and adult size within sex and treatment increased at decreasing food availability. The results suggest that although the studied population has adapted to a wide range of food availabilities, both scarcity and excess of food have important negative impacts on fitness.

Mosquito-borne arboviruses of African origin: review of key viruses and vectors

Key aspects of 36 mosquito-borne arboviruses indigenous to Africa are summarized, including lesser or poorly-known viruses which, like Zika, may have the potential to escape current sylvatic cycling to achieve greater geographical distribution and medical importance. Major vectors are indicated as well as reservoir hosts, where known. A series of current and future risk factors is addressed. It is apparent that Africa has been the source of most of the major mosquito-borne viruses of medical importance that currently constitute serious global public health threats, but that there are several other viruses with potential for international challenge. The conclusion reached is that increased human population growth in decades ahead coupled with increased international travel and trade is likely to sustain and increase the threat of further geographical spread of current and new arboviral disease.

Oropouche fever, an emergent disease from the Americas

Oropouche virus is the aetiological agent of Oropouche fever, a zoonotic disease mainly transmitted by midges of the species Culicoides paraensis. Although the virus was discovered in 1955, more attention has been given recently to both the virus and the disease due to outbreaks of Oropouche fever in different areas of Brazil and Peru. Serological studies in human and wild mammals have also found Oropouche virus in Argentina, Bolivia, Colombia, and Ecuador. Several mammals act as reservoirs of the disease, although the sylvatic cycle of Oropouche virus remains to be assessed properly. Oropouche fever lacks key symptoms to be differentiated from other arboviral febrile illnesses from the Americas. Sporadic cases of aseptic meningitis have also been described with good prognosis. Habitat loss can increase the likelihood of Oropouche virus emergence in the short-term in South America.

Ecological niche modeling of rabies in the changing Arctic of Alaska

BACKGROUND

Rabies is a disease of global significance including in the circumpolar Arctic. In Alaska enzootic rabies persist in northern and western coastal areas. Only sporadic cases have occurred in areas outside of the regions considered enzootic for the virus, such as the interior of the state and urbanized regions.

RESULTS

Here we examine the distribution of diagnosed rabies cases in Alaska, explicit in space and time. We use a geographic information system (GIS), 20 environmental data layers and provide a quantitative non-parsimonious estimate of the predicted ecological niche, based on data mining, machine learning and open access data. We identify ecological correlates and possible drivers that determine the ecological niche of rabies virus in Alaska. More specifically, our models show that rabies cases are closely associated with human infrastructure, and reveal an ecological niche in remote northern wilderness areas. Furthermore a model utilizing climate modeling suggests a reduction of the current ecological niche for detection of rabies virus in Alaska, a state that is disproportionately affected by a changing climate.

CONCLUSIONS

Our results may help to better inform public health decisions in the future and guide further studies on individual drivers of rabies distribution in the Arctic.

Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling

Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks.

Zika Virus, Elevation, and Transmission Risk

INTRODUCTION

Zika virus has appeared in the Americas in the form of a major outbreak, and is now known to cause birth defects when pregnant women are infected. As a result, the Centers for Disease Control and Prevention issued travel guidelines, in the form of an elevational risk definition: destinations below 2000m are considered as at-risk.

METHODS

We explored the distribution of known Zika virus vector mosquito species in relation to climatic conditions, elevation, latitude, and air traffic connections to the United States.

RESULTS

In view of the tropical and subtropical nature of the mosquito species that are the primary Zika virus vectors, we point out that climate varies rather dramatically with respect to elevation and latitude, such that a single elevational criterion will be a poor predictor of potential for transmission.

DISCUSSION

We suggest an initial adjustment would consider latitude in addition to elevation; a more definitive, quantitative analysis of risk would consider variables of ecology, climate, human condition, and connectivity of areas.