Diversity, Co-Occurrence, and Nestedness Patterns of Sand Fly Species (Diptera: Psychodidae) in Two Rural Areas of Western Panamá.

Cutaneous Leishmaniasis transmission in the New World is observed in areas with rich sand fly species' faunas. The diversity and composition of sand fly species can change in response to seasonal weather and land use changes. Here, we present results from a two-year-long study where we collected, using Centers for Disease Control (CDC) light traps, sand flies from two rural areas, Las Pavas (LP) and Trinidad de las Minas (T) in western Panamá. Over 710 trap-nights, we collected 16,156 sand flies from 15 genera and 35 species. We identified 34 species in T, and the most abundant species collected was Nyssomyia trapidoi (Fairchild and Hertig, 1952) (n = 2278, 37%), followed by Psychodopygus panamensis (Shannon, 1926) (n = 1112, 18%), and Trichopygomyia triramula (Fairchild and Hertig, 1952) (n = 1063, 17%). In LP, we identified 26 species, and the most abundant species collected were Ty. triramula (n = 4729, 48%), and Ps. panamensis (n = 3444, 35%). We estimated a higher species' richness in T (Chao2 ± S.E.: 36.58 ± 3.84) than in LP (27.49 ± 2.28). In T, species' richness was significantly higher in the rainy season, but no seasonal differences were observed in LP. Species' assemblages were nested in the two areas. Phlebotomine sand fly species' abundance increased at the two sites during the rainy season. Our data suggest that seasonality is more important than land use as a factor driving sand fly species' diversity at the studied sites.

Predicting West Nile Virus Infection Risk From the Synergistic Effects of Rainfall and Temperature.

Mosquito-based surveillance is a practical way to estimate the risk of transmission of West Nile virus (WNV) to people. Variations in temperature and precipitation play a role in driving mosquito infection rates and transmission of WNV, motivating efforts to predict infection rates based on prior weather conditions. Weather conditions and sequential patterns of meteorological events can have particularly important, but regionally distinctive, consequences for WNV transmission, with high temperatures and low precipitation often increasing WNV mosquito infection. Predictive models that incorporate weather can thus be used to provide early indications of the risk of WNV infection. The purpose of this study was first, to assess the ability of a previously published model of WNV mosquito infection to predict infection for an area within the region for which it was developed, and second, to improve the predictive ability of this model by incorporating new weather factors that may affect mosquito development. The legacy model captured the primary trends in mosquito infection, but it was improved considerably when calibrated with local mosquito infection rates. The use of interaction terms between precipitation and temperature improved model performance. Specifically, temperature had a stronger influence than rainfall, so that lower than average temperature greatly reduced the effect of low rainfall on increased infection rates. When rainfall was lower, high temperature had an even stronger positive impact on infection rates. The final model is practical, stable, and operationally valid for predicting West Nile virus infection rates in future weeks when calibrated with local data.

New and Common Haplotypes Shape Genetic Diversity in Asian Tiger Mosquito Populations from Costa Rica and Panamá.

The Asian tiger mosquito, Aedes albopictus (Skuse) (Diptera: Culicidae), is a vector of several human pathogens. Ae. albopictus is also an invasive species that, over recent years, has expanded its range out of its native Asia. Ae. albopictus was suspected to be present in Central America since the 1990s, and its presence was confirmed by most Central American nations by 2010. Recently, this species has been regularly found, yet in low numbers, in limited areas of Panamá and Costa Rica (CR). Here, we report that short sequences (∼558 bp) of the mitochondrial cytochrome oxidase subunit 1 (COI) and NADH dehydrogenase subunit 5 genes of Ae. albopictus, had no haplotype diversity. Instead, there was a common haplotype for each gene in both CR and Panamá. In contrast, a long COI sequence (∼1,390 bp) revealed that haplotype diversity (±SD) was relatively high in CR (0.72±0.04) when compared with Panamá (0.33±0.13), below the global estimate for reported samples (0.89±0.01). The long COI sequence allowed us to identify seven (five new) haplotypes in CR and two (one new) in Panamá. A haplotype network for the long COI gene sequence showed that samples from CR and Panamá belong to a single large group. The long COI gene sequences suggest that haplotypes in Panamá and CR, although similar to each other, had a significant geographic differentiation (Kst=1.33; P<0.001). Thus, most of our results suggest a recent range expansion in CR and Panamá.

Population dynamics of Armigeres subalbatus (Diptera: Culicidae) across a temperate altitudinal gradient.

Understanding the impacts of weather fluctuations, and environmental gradients, on the abundance of vectors is fundamental to grasp the dynamic nature of the entomological risk for disease transmission. The mosquito Armigeres subalbatus (Coquillet) is a common vector of filariasis. Nevertheless, its population dynamics have been relatively poorly studied. Here, we present results from a season long study where we studied spatio-temporal abundance patterns of Ar. subalbatus across the altitudinal gradient of Mt. Konpira in Nagasaki, Japan. Spatially, we found that abundance of adult Ar. subalbatus decreased with altitude and increased in areas where the ground was rich in leaf litter. Similarly, adult activity was observed only when relative humidity was over 65%. Temporally, we found that peaks in abundance followed large rainfall events. Nevertheless, this mosquito was under significant density dependence regulation. Our results suggest that Ar. subalbatus population peaks following large rainfall events could reflect the recruitment of individuals that were dormant as dry eggs. We did not find a clear signal of temperature on abundance changes of this mosquito, but only on its phenology. Since ground cover seemed more critical than temperature to its spatial distribution, we propose that this mosquito might have some degree of autonomy to changes in temperature.

Domestic dog health worsens with socio-economic deprivation of their home communities.

Dogs play an important role in infectious disease transmission as reservoir hosts of many zoonotic and wildlife pathogens. Nevertheless, unlike wildlife species involved in the life cycle of pathogens, whose health status might be a direct reflection of their fitness and competitive abilities, dog health condition could be sensitive to socio-economic factors impacting the well-being of their owners. Here, we compare several dog health indicators in three rural communities of Panama with different degrees of socio-economic deprivation. From a total of 78 individuals, we collected blood and fecal samples, and assessed their body condition. With the blood samples, we performed routine hematologic evaluation (complete blood counts) and measured cytokine levels (Interferon-γ and Interleukin-10) through enzyme-linked immunosorbent assays. With the fecal samples we diagnosed helminthiases. Dogs were also serologically tested for exposure to Trypanosoma cruzi and canine distemper virus, and molecular tests were done to assess T. cruzi infection status. We found significant differences between dog health measurements, pathogen prevalence, parasite richness, and economic status of the human communities where the dogs lived. We found dogs that were less healthy, more likely to be infected with zoonotic pathogens, and more likely to be seropositive to canine distemper virus in the communities with lower economic status. This study concludes that isolated communities of lower economic status in Panama may have less healthy dogs that could become major reservoirs in the transmission of diseases to humans and sympatric wildlife.

Weather variability impacts on oviposition dynamics of the southern house mosquito at intermediate time scales.

Oviposition is a major event in the life history of mosquitoes, shaping both individual fitness and vectorial capacity. Several exogenous factors have been shown as important for the dynamic forcing of oviposition at finely (hourly) and coarsely (monthly or season to season) grained temporal scales. However, field studies addressing the interplay of weather factors on oviposition dynamics at the intermediate (days to weeks) time scale are missing. Here, we present the results from a field study that showed the oviposition dynamics of the southern house mosquito, Culex quinquefasciatus Say (Diptera: Culicidae), to be: (i) primarily dictated by relative humidity; and (ii) disrupted by rainfall events that resulted in a modified sensitivity to relative humidity. Rainfall changed the concentration of ammonia, a major limiting resource of microbes used as food by mosquito larvae. Following major rainfall events, the importance of relative humidity in forcing the oviposition dynamics also changed. Finally, our results indicate that qualitative changes in oviposition habitats modify the importance of weather variables as predictors of mosquito oviposition dynamics.

Climate and recruitment limitation of hosts: the dynamics of American cutaneous leishmaniasis seen through semi-mechanistic seasonal models.

Diseases cycle as a response to endogenous and exogenous factors. For infectious diseases caused by vector-transmitted pathogens, the exogenous factors are commonly equated to climatic forces and the endogenous factors to the recruitment of new susceptible individuals. Mathematical models that explicitly (parametrically) consider both types of factor are, however, very rare. An approach is presented to model the effects of endogenous and exogenous factors parametrically, using a time series for American cutaneous leishmaniasis (ACL) from Costa Rica. The seasonality of the disease is modelled using a seasonal autoregressive approach. The latter has the advantage of allowing the use of semi-mechanistic frameworks that consider infection clearance, while explicitly introducing the feedbacks produced by the transition between immune classes, as well as climatic forcing. It also uses a relatively small number of degrees of freedom (compared with the numbers involved in semi-parametric approaches), making it useful for relatively short time series and series with abrupt changes. Compared with non-mechanistic models built for prediction purposes, this way of modelling seems to increase the likelihood of the data being explained by a plausible mechanism. The approach used in this study of ACL could be useful in investigating the changes that occur in other diseases that show non-stationary seasonal dynamics, and can be easily adapted to model the dynamics of other infectious diseases that show trends or breakpoints. The present results support the view that humans affected by ACL are mostly incidental hosts, and indicate that, at the population level, there is a delay of about 5 months between human infection with the causative parasites and the onset of clinical symptoms. They encourage the development of surveillance systems, for monitoring the prevalence of infection in the sandflies that act as vectors, and the use of sentinel hosts, so that control measures can be rapidly applied or strengthened before a serious outbreak occurs. The development of more accurate mathematical models of ACL will depend largely on advances in the ecology of the disease and of all the hosts of the causative parasites.

Shifting patterns: malaria dynamics and rainfall variability in an African highland.

The long-term patterns of malaria in the East African highlands typically involve not only a general upward trend in cases but also a dramatic increase in the size of epidemic outbreaks. The role of climate variability in driving epidemic cycles at interannual time scales remains controversial, in part because it has been seen as conflicting with the alternative explanation of purely endogenous cycles exclusively generated by the nonlinear dynamics of the disease. We analyse a long temporal record of monthly cases from 1970 to 2003 in a highland of western Kenya with both a time-series epidemiological model (time-series susceptible-infected-recovered) and a statistical approach specifically developed for non-stationary patterns. Results show that multiyear cycles of malaria outbreaks appear in the 1980s, concomitant with the timing of a regime shift in the dynamics of cases; the cycles become more pronounced in the 1990s, when the coupling between disease and rainfall is also stronger as the variance of rainfall increased at the frequencies of coupling. Disease dynamics and climate forcing play complementary and interacting roles at different temporal scales. Thus, these mechanisms should not be viewed as alternative and their interaction needs to be integrated in the development of future predictive models.

Malaria resurgence in the East African highlands: temperature trends revisited.

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models.