Seasonal population dynamics and behaviour of insects in models of vector-borne pathogens

Spatio-Temporal distribution of a vector of cutaneous leishmaniasis: Pintomyia longiflocosa, in a population from the Colombian Andean Mountains

BACKGROUND

Leishmaniasis, a neglected disease and public health concern, is associated with various factors such as biological, social, economical conditions and climate, increasing the risk of human infection. Understanding the population dynamics of the vectors, like Pintomyia longiflocosa, and its relationship with ecological variables is crucial for developing effective strategies to control sand fly populations and combat cutaneous leishmaniasis in a tropical country like Colombia.

METHODOLOGY

Adult sand flies were collected in three different sample locations: outdoor, indoor, and peri-domestic areas in three houses located in the rural settlement of Campoalegre (Huila) between February 2020 and February 2021, using the CDC light traps. The sand fly density was quantified and associated with the sample locations and the sampling months using Analysis of Variance and Pearson correlations.

PRINCIPAL FINDINGS

In the period of the sample, 98.86% of sand fly collected was identified as Pi. longiflocosa. The density of this species was significantly different between males and females, the latter contributing more to density in all sample locations (P<0.0001). The outdoor was the sample location with the highest and most significative density in this study (70%, P = 0.04). The density of these sand flies is related to the seasonality of Campoalegre, revealing a density peak from February and June to October (P < 0.05). Finally, precipitation is the environmental variable prominently linked to the density pattern, showing a negative correlation with it. Months with the highest precipitations show the lowest values of Pi. longiflocosa abundance.

CONCLUSIONS/SIGNICANCE

Our investigation reveals a inverse correlation between precipitation levels and the abundance of Pi. longiflocosa in Campoalegre (Huila), particularly in outdoor areas. This suggests that vector control strategies to periods of reduced precipitation in outdoor settings could offer an effective approach to minimizing cases of cutaneous leishmaniasis in the region.

Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India

Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52–4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance–a key component of JE hazard–over large spatial scales, providing decision-makers with better guidance for targeting vector surveillance and control efforts.

Japanese encephalitis (JE) is the leading cause of viral encephalopathy in Asia with an estimated 100,000 annual cases and 25,000 deaths. However, insufficient data on the predominant mosquito vector Culex tritaeniorhynchus–a key component of JE hazard–precludes hazard estimation required to target public health interventions. Previous studies have provided limited estimates of JE hazard, often predicting geographic distributions of potential vector occurrence without accounting for vector abundance, seasonality, or uncertainty in predictions. This study details a novel approach to predict spatiotemporal patterns in JE vector abundance using a joint-likelihood modelling technique that leverages information from sparse vector surveillance data. We showed that patterns in JE vector abundance were driven by seasonality and environmental factors and so demonstrated the limitations of previously available static vector distribution maps in estimating the vector population component of JE hazard. One-month lagged vector abundance predictions showed a positive relationship with JE outbreaks, signalling the potential use of vector abundance as a proxy for JE hazard. While vector surveillance data are limited, joint-likelihood models offer a useful approach to inform vector abundance predictions. This study provides decision-makers with a more complete picture of the distribution of JE vector abundance and can be used to target vector surveillance and control efforts and enhance the allocation of resources.

Vector Specificity of Arbovirus Transmission

More than 25% of human infectious diseases are vector-borne diseases (VBDs). These diseases, caused by pathogens shared between animals and humans, are a growing threat to global health with more than 2.5 million annual deaths. Mosquitoes and ticks are the main vectors of arboviruses including flaviviruses, which greatly affect humans. However, all tick or mosquito species are not able to transmit all viruses, suggesting important molecular mechanisms regulating viral infection, dissemination, and transmission by vectors. Despite the large distribution of arthropods (mosquitoes and ticks) and arboviruses, only a few pairings of arthropods (family, genus, and population) and viruses (family, genus, and genotype) successfully transmit. Here, we review the factors that might limit pathogen transmission: internal (vector genetics, immune responses, microbiome including insect-specific viruses, and coinfections) and external, either biotic (adult and larvae nutrition) or abiotic (temperature, chemicals, and altitude). This review will demonstrate the dynamic nature and complexity of virus–vector interactions to help in designing appropriate practices in surveillance and prevention to reduce VBD threats.

Human-Mosquito Contact: A Missing Link in Our Understanding of Mosquito-Borne Disease Transmission Dynamics

Despite the critical role that contact between hosts and vectors, through vector bites, plays in driving vector-borne disease (VBD) transmission, transmission risk is primarily studied through the lens of vector density and overlooks host-vector contact dynamics. This review article synthesizes current knowledge of host-vector contact with an emphasis on mosquito bites. It provides a framework including biological and mathematical definitions of host-mosquito contact rate, blood-feeding rate, and per capita biting rates. We describe how contact rates vary and how this variation is influenced by mosquito and vertebrate factors. Our framework challenges a classic assumption that mosquitoes bite at a fixed rate determined by the duration of their gonotrophic cycle. We explore alternative ecological assumptions based on the functional response, blood index, forage ratio, and ideal free distribution within a mechanistic host-vector contact model. We highlight that host-vector contact is a critical parameter that integrates many factors driving disease transmission. A renewed focus on contact dynamics between hosts and vectors will contribute new insights into the mechanisms behind VBD spread and emergence that are sorely lacking. Given the framework for including contact rates as an explicit component of mathematical models of VBD, as well as different methods to study contact rates empirically to move the field forward, researchers should explicitly test contact rate models with empirical studies. Such integrative studies promise to enhance understanding of extrinsic and intrinsic factors affecting host-vector contact rates and thus are critical to understand both the mechanisms driving VBD emergence and guiding their prevention and control.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Pathogens manipulate the preference of vectors, slowing disease spread in a multi-host system

The spread of vector-borne pathogens depends on a complex set of interactions among pathogen, vector, and host. In single-host systems, pathogens can induce changes in vector preferences for infected vs. healthy hosts. Yet it is unclear if pathogens also induce changes in vector preference among host species, and how changes in vector behaviour alter the ecological dynamics of disease spread. Here, we couple multi-host preference experiments with a novel model of vector preference general to both single and multi-host communities. We show that viruliferous aphids exhibit strong preferences for healthy and long-lived hosts. Coupling experimental results with modelling to account for preference leads to a strong decrease in overall pathogen spread through multi-host communities due to non-random sorting of viruliferous vectors between preferred and non-preferred host species. Our results demonstrate the importance of the interplay between vector behaviour and host diversity as a key mechanism in the spread of vectored-diseases.

Uncovering mechanisms behind mosquito seasonality by integrating mathematical models and daily empirical population data: Culex pipiens in the UK

BACKGROUND

Many mosquito-borne diseases exhibit substantial seasonality, due to strong links between environmental variables and vector and pathogen life-cycles. Further, a range of density-dependent and density-independent biotic and abiotic processes affect the phenology of mosquito populations, with potentially large knock-on effects for vector dynamics and disease transmission. Whilst it is understood that density-independent and density-dependent processes affect seasonal population levels, it is not clear how these interact temporally to shape the population peaks and troughs. Due to this, the paucity of high-resolution data for validation, and the difficulty of parameterizing density-dependent processes, models of vector dynamics may poorly estimate abundances, which has knock-on effects for our ability predict vector-borne disease outbreaks.

RESULTS

We present a rich dataset describing seasonal abundance patterns of each life stage of Culex pipiens, a widespread vector of West Nile virus, at a field site in southern England in 2015. Abundance of immature stages was measured three times per week, whilst adult traps were run four nights each week. This dataset is integrated with an existing delay-differential equation model predicting Cx. pipiens seasonal abundance to improve understanding of observed seasonal abundance patterns. At our field site, the outcome of our model fitting suggests interspecific predation on mosquito larvae and temperature-dependent larval mortality combine to act as the main sources of population regulation throughout the active season, whilst competition for resources is a relatively small source of larval mortality.

CONCLUSIONS

The model suggests that density-independent mortality and interspecific predation interact to shape patterns of mosquito seasonal abundance in a permanent aquatic habitat and we propose that competition for resources is likely to be important where periods of high rainfall create transient habitats. Further, we highlight the importance of challenging population abundance models with data from across all life stages of the species of interest if reliable inferences are to be drawn from these models, particularly when considering mosquito control and vector-borne disease transmission.

Management of arthropod vector data - Social and ecological dynamics facing the One Health perspective

Emerging infectious diseases (EIDs) are spread by direct and/or indirect contacts between a pathogen or parasite and their hosts. Arthropod vectors have evolved as excellent bloodsuckers, providing an elegant transportation mode for a wide number of infectious agents. The nature of pathogen and parasite transfer and the models used to predict how a disease might spread are magnified in complexity when an arthropod vector is part of the disease cycle. One Health is a worldwide strategy for expanding interdisciplinary collaborations and communications in all aspects of health care for humans, animals and the environment. It would benefit from a structured analysis to address vectoring of arthropod-borne diseases as a dynamic transactional process. This review focused on how arthropod vector data can be used to better model and predict zoonotic disease outbreaks. With enhanced knowledge to describe arthropod vector disease transfer, researchers will have a better understanding about how to model disease outbreaks. As public health research evolves to include more social-ecological systems, the roles of society, ecology, epidemiology, pathogen/parasite evolution and animal behavior can be better captured in the research design. Overall, because of more collaborative data collection processes on arthropod vectors, disease modeling can better predict conditions where EIDs will occur.

Association of vectors and environmental conditions during the emergence of Peruvian horse sickness orbivirus and Yunnan orbivirus in northern Peru

Since 1983, cases of diseased donkeys and horses with symptoms similar to those produced by alphaviruses were identified in two departments in northern Peru; however serological testing ruled out the presence of those viruses and attempts to isolate an agent were also unproductive. In 1997, also in northern Peru, two new orbiviruses were discovered, each recognized as a causative agent of neurological diseases in livestock and domestic animals and, at the same time, mosquitoes were found to be infected with these viruses. Peruvian horse sickness virus (PHSV) was isolated from pools of culicid mosquitoes, Aedes serratus and Psorophora ferox, and Yunnan virus (YUOV) was isolated from Aedes scapularis in the subtropical jungle (upper jungle) located on the slope between the east side of the Andes and the Amazonian basin in the Department of San Martín. Both viruses later were recovered from mosquitoes collected above the slope between the west side of the Andes and the coast (Department of Piura) in humid subtropical areas associated with the Piura River basin. In this region, PHSV was isolated from Anopheles albimanus and YUOV was isolated from Ae. scapularis. We discuss the ecology of vector mosquitoes during the outbreaks in the areas where these mosquitoes were found.

Vector host-feeding preferences drive transmission of multi-host pathogens: West Nile virus as a model system

Seasonal epizootics of vector-borne pathogens infecting multiple species are ecologically complex and difficult to forecast. Pathogen transmission potential within the host community is determined by the relative abilities of host species to maintain and transmit the pathogen and by ecological factors influencing contact rates between hosts and vectors. Increasing evidence of strong feeding preferences by a number of vectors suggests that the host community experienced by the pathogen may be very different from the local host community. We developed an empirically informed transmission model for West Nile virus (WNV) in four sites using one vector species (Culex pipiens) and preferred and non-preferred avian hosts. We measured strong feeding preferences for American robins (Turdus migratorius) by Cx. pipiens, quantified as the proportion of Cx. pipiens blood meals from robins in relation to their abundance (feeding index). The model accurately predicted WNV prevalence in Cx. pipiens at three of four sites. Sensitivity analysis revealed feeding preference was the most influential parameter on intensity and timing of peak WNV infection in Cx. pipiens and a threshold feeding index for transmission was identified. Our findings indicate host preference-induced contact heterogeneity is a key mediator of vector-borne pathogen epizootics in multi-species host communities, and should be incorporated into multi-host transmission models.

Modeling transmission dynamics and control of vector-borne neglected tropical diseases

Neglected tropical diseases affect more than one billion people worldwide. The populations most impacted by such diseases are typically the most resource-limited. Mathematical modeling of disease transmission and cost-effectiveness analyses can play a central role in maximizing the utility of limited resources for neglected tropical diseases. We review the contributions that mathematical modeling has made to optimizing intervention strategies of vector-borne neglected diseases. We propose directions forward in the modeling of these diseases, including integrating new knowledge of vector and pathogen ecology, incorporating evolutionary responses to interventions, and expanding the scope of sensitivity analysis in order to achieve robust results.

Impact of insecticide interventions on the abundance and resistance profile of Aedes aegypti

Insecticide-based vector control is the primary strategy for curtailing dengue transmission. We used a mathematical model of the seasonal population dynamics of the dengue mosquito vector, Aedes aegypti, both to assess the effectiveness of insecticide interventions on reducing adult mosquito abundance and to predict evolutionary trajectories of insecticide resistance. We evaluated interventions that target larvae, adults, or both. We found that larval control and adult control using ultra-low-volume insecticide applications can reduce adult mosquito abundance with effectiveness that depends on the frequency of applications. We also found that year-long continuous larval control and adult control, using either insecticide treatment of surfaces and materials or lethal ovitraps, imposed the greatest selection for resistance. We demonstrated that combined targeting of larvae and adults at the start of the dengue season is optimal. This intervention contrasts with year-long continuous larval control policies adopted in settings in which dengue transmission occurs.

Using a climate-dependent model to predict mosquito abundance: application to Aedes (Stegomyia) africanus and Aedes (Diceromyia) furcifer (Diptera: Culicidae)

Mosquitoes, acting as vectors, are involved in the transmission of viruses. Thus, their abundances, which strongly depend on the weather and environment, are closely linked to major disease outbreaks. The aim of this paper is to provide a tool to predict vector abundance. In order to describe the dynamics of mosquito populations, we developed a matrix model integrating climate fluctuations. The population is structured in five stages: two egg stages (immature and mature), one larval stage and two female flying stages (nulliparous and parous). The water availability in breeding sites was considered as the main environmental factor affecting the mosquito life-cycle. Thus, the model represents the evolution of the mosquito abundance in each stage over time, in connection with water availability. The model was used to simulate the abundance trends over 3 years of two mosquito species, Aedes africanus (Theobald) and Aedes furcifer (Edwards), vectors of the yellow fever virus in Ivory Coast. As both these species breed in tree holes, the water dynamics in the tree hole was reproduced from daily rainfall data. The results we obtained showed a good match between the simulated populations and the field data over the time period considered.

Seasonal density fluctuations of the exotic ornate kangaroo tick, Amblyomma triguttatum triguttatum Koch, and its distribution on Yorke Peninsula, South Australia

The ornate kangaroo tick, Amblyomma triguttatum triguttatum, was recently recorded on southern Yorke Peninsula, South Australia. We examined seasonal fluctuations in A. triguttatum triguttatum life stages (adult, larva, and nymph) and its distribution on the peninsula. We used in situ CO2 traps and dragging cloths to determine monthly fluctuations in free-living ticks at four sites at Innes National Park from January to December 2006, and to determine the tick's distribution on wider Yorke Peninsula. At each site, 166 m2 of ground surface were directly sampled with cloths, representing 16 1-m2 CO2 stations and three 1-m-wide 50-m transects. Adult A. triguttatum triguttatum were present in January and February and from August to December, with a peak (n=54) occurring in November. Larvae were present from February to August, with their highest density (n=3067) detected in March. Nymphs were collected from January to April and from August to December, with the highest density (n=61) detected in September. Overall, A. triguttatum triguttatum numbers were highest in March when larvae peaked, and few ticks were detected in January (summer) or July (winter). Ticks occurred at several sites on southern Yorke Peninsula, supporting their invasive status.