A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970-2010

Present and future suitability of invasive and urban vectors through an environmentally driven mosquito reproduction number

Temperature and water availability significantly influence mosquito population dynamics. We have developed a method, integrating experimental data with insights from mosquito and thermal biology, to calculate the basic reproduction number ([Formula: see text]) for urban mosquito species Aedes albopictus and Aedes aegypti. [Formula: see text] represents the number of female mosquitoes produced by one female during her lifespan, indicating suitability for growth. Environmental conditions, including temperature, rainfall and human density, influence [Formula: see text] by altering key mosquito life cycle traits. Validation using data from Spain and Europe confirms the approach's reliability. Our analysis suggests that temperature increases may not uniformly benefit Ae. albopictus proliferation but could boost Ae. aegypti expansion. We suggest using vector [Formula: see text] maps, leveraging climate and environmental data, to predict areas susceptible to invasive mosquito population growth. These maps aid resource allocation for intervention strategies, supporting effective vector surveillance and management efforts.

Modeling spatiotemporal dynamics of Amblyomma americanum questing activity in the central Great Plains

Ticks represent important vectors of a number of bacterial and viral disease agents, owing to their hematophagous nature and their questing behavior (the process in which they seek new hosts). Questing activity is notably seasonal with spatiotemporal dynamics that needs to be understood in detail as part of mediating and mitigating tick-borne disease risk. Models of the geography of tick questing activity developed to date, however, have ignored the temporal dimensions of that behavior; more fundamentally, they have often not considered the sampling underlying available occurrence data. Here, we have addressed these shortfalls for Amblyomma americanum, the most commonly encountered tick in the central Great Plains, via (1) detailed, longitudinal sampling to characterize the spatiotemporal dimensions of tick questing activity; (2) randomization tests to establish in which environmental dimensions a species is manifesting selective use; and (3) modeling methods that include both presence data and absence data, taking fullest advantage of the information available in the data resource. The outcome was a detailed picture of geographic and temporal variation in suitability for the species through the two-year course of this study. Such models that take full advantage of available information will be crucial in understanding the risk of tick-borne disease into the future.

Estimating dynamics of dengue disease in Colombo district of Sri Lanka with environmental impact by quantifying the per-capita vector density

Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes causing a global threat to health, social, and economic sectors in many of the tropical countries including Sri Lanka. In Sri Lanka, the tropical climate, marked by seasonal weather primarily influenced by monsoons, fosters optimal conditions for the virus to spread efficiently. This heightened transmission results in increased per-capita vector density. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district - the geographical region with the highest recorded dengue threat in Sri Lanka. An iterative approach is employed to dynamically estimate dengue cases leveraging the Markov chain Monte Carlo simulations, utilizing the dynamics of four seasons per year influenced by monsoon weather patterns governing in the region. The developed algorithm allows to estimate the risk of dengue outbreaks in 2017 and 2019 with high precision, facilitating accurate forecasts of upcoming disease emergence patterns for better preparedness. The uncertainty quantification not only validated the accuracy of outbreak estimates but also showcased the model's capacity to capture extreme cases and revealed undisclosed external factors such as human mobility and environmental pollution that might affect dengue transmission in the Colombo district of Sri Lanka.

A global mathematical model of climatic suitability for Plasmodium falciparum malaria

Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control. Previous work has developed mathematical models and global maps for the suitability of temperature for malaria transmission. In this paper, existing temperature-based models are extended to include two other important bioclimatic factors: humidity and rainfall. This model is combined with fine spatial resolution climatic data to produce a more biologically-realistic global map of climatic suitability for Plasmodium falciparum malaria. The climatic suitability index developed corresponds more closely than previous temperature suitability indices with the global distribution of P. falciparum malaria. There is weak agreement between the Malaria Atlas Project estimates of P. falciparum prevalence in Africa and the estimates of suitability solely based on temperature (Spearman Correlation coefficient of ρ = 0.24 ). The addition of humidity and then rainfall improves the comparison ( ρ = 0.62 when humidity added; ρ = 0.70 when both humidity and rainfall added). By incorporating the impacts of humidity and rainfall, this model identifies arid regions that are not climatically suitable for transmission of P. falciparum malaria. Incorporation of this improved index of climatic suitability into geospatial models can improve global estimates of malaria prevalence and transmission intensity.

Ronald Ross: Pioneer of Malaria Research and Nobel Laureate

Sir Ronald Ross, a British medical doctor and researcher, is renowned for his pioneering work in malaria research. His discovery of the malaria parasite's lifecycle within mosquitoes revolutionized the understanding and control of malaria, transitioning the field from the miasma theory to vector-based strategies. This literature review aims to explore the comprehensive contributions of Ronald Ross to malaria research and their enduring impact on public health.

A hybrid Lagrangian-Eulerian model for vector-borne diseases

In this paper, a multi-patch and multi-group vector-borne disease model is proposed to study the effects of host commuting (Lagrangian approach) and/or vector migration (Eulerian approach) on disease spread. We first define the basic reproduction number of the model, R 0 , which completely determines the global dynamics of the model system. Namely, ifR 0 ≤ 1 , then the disease-free equilibrium is globally asymptotically stable, and ifR 0 > 1 , then there exists a unique endemic equilibrium which is globally asymptotically stable. Then, we show that the basic reproduction number has lower and upper bounds which are independent of the host residence times matrix and the vector migration matrix. In particular, nonhomogeneous mixing of hosts and vectors in a homogeneous environment generally increases disease persistence and the basic reproduction number of the model attains its minimum when the distributions of hosts and vectors are proportional. Moreover, R 0 can also be estimated by the basic reproduction numbers of disconnected patches if the environment is homogeneous. The optimal vector control strategy is obtained for a special scenario. In the two-patch and two-group case, we numerically analyze the dependence of the basic reproduction number and the total number of infected people on the host residence times matrix and illustrate the optimal vector control strategy in homogeneous and heterogeneous environments.

Transmission dynamics of Zika virus with multiple infection routes and a case study in Brazil

The Zika virus (ZIKV) is a serious global public health crisis. A major control challenge is its multiple transmission modes. This paper aims to simulate the transmission patterns of ZIKV using a dynamic process-based epidemiological model written in ordinary differential equations, which incorporates the human-to-mosquito infection by bites and sewage, mosquito-to-human infection by bites, and human-to-human infection by sex. Mathematical analyses are carried out to calculate the basic reproduction number and backward bifurcation, and prove the existence and stability of the equilibria. The model is validated with infection data by applying it to the 2015-2016 ZIKV epidemic in Brazil. The results indicate that the reproduction number is estimated to be 2.13, in which the contributions by mosquito bite, sex and sewage account for 85.7%, 3.5% and 10.8%, respectively. This number and the morbidity rate are most sensitive to parameters related to mosquito ecology, rather than asymptomatic or human-to-human transmission. Multiple transmission routes and suitable temperature exacerbate ZIKV infection in Brazil, and the vast majority of human infection cases were prevented by the intervention implemented. These findings may provide new insights to improve the risk assessment of ZIKV infection.

Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Mathematical models within the Ross-Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond to emerging threats. These threats are typically characterized by a high degree of heterogeneity, introducing a range of possible complexities in models and challenges to maintain the link with empirical evidence. We systematically identified and analysed a total of 77 published papers presenting compartmental West Nile virus (WNV) models that use parameter values derived from empirical studies. Using a set of 15 criteria, we measured the dissimilarity compared with the Ross-Macdonald framework. We also retrieved the purpose and type of models and traced the empirical sources of their parameters. Our review highlights the increasing refinements in WNV models. Models for prediction included the highest number of refinements. We found uneven distributions of refinements and of evidence for parameter values. We identified several challenges in parametrizing such increasingly complex models. For parameters common to most models, we also synthesize the empirical evidence for their values and ranges. The study highlights the potential to improve the quality of WNV models and their applicability for policy by establishing closer collaboration between mathematical modelling and empirical work.

An analytically tractable, age-structured model of the impact of vector control on mosquito-transmitted infections

Vector control is a vital tool utilised by malaria control and elimination programmes worldwide, and as such it is important that we can accurately quantify the expected public health impact of these methods. There are very few previous models that consider vector-control-induced changes in the age-structure of the vector population and the resulting impact on transmission. We analytically derive the steady-state solution of a novel age-structured deterministic compartmental model describing the mosquito feeding cycle, with mosquito age represented discretely by parity-the number of cycles (or successful bloodmeals) completed. Our key model output comprises an explicit, analytically tractable solution that can be used to directly quantify key transmission statistics, such as the effective reproductive ratio under control, Rc, and investigate the age-structured impact of vector control. Application of this model reinforces current knowledge that adult-acting interventions, such as indoor residual spraying of insecticides (IRS) or long-lasting insecticidal nets (LLINs), can be highly effective at reducing transmission, due to the dual effects of repelling and killing mosquitoes. We also demonstrate how larval measures can be implemented in addition to adult-acting measures to reduce Rc and mitigate the impact of waning insecticidal efficacy, as well as how mid-ranges of LLIN coverage are likely to experience the largest effect of reduced net integrity on transmission. We conclude that whilst well-maintained adult-acting vector control measures are substantially more effective than larval-based interventions, incorporating larval control in existing LLIN or IRS programmes could substantially reduce transmission and help mitigate any waning effects of adult-acting measures.

Quantifying Rift Valley fever virus transmission efficiency in a lamb-mosquito-lamb model

Rift Valley fever virus (RVFV) is a (re)emerging mosquito-borne pathogen impacting human and animal health. How RVFV spreads through a population depends on population-level and individual-level interactions between vector, host and pathogen. Here, we estimated the probability for RVFV to transmit to naive animals by experimentally exposing lambs to a bite of an infectious mosquito, and assessed if and how RVFV infection subsequently developed in the exposed animal. Aedes aegypti mosquitoes, previously infected via feeding on a viremic lamb, were used to expose naive lambs to the virus. Aedes aegypti colony mosquitoes were used as they are easy to maintain and readily feed in captivity. Other mosquito spp. could be examined with similar methodology. Lambs were exposed to either 1-3 (low exposure) or 7-9 (high exposure) infectious mosquitoes. All lambs in the high exposure group became viremic and showed characteristic signs of Rift Valley fever within 2-4 days post exposure. In contrast, 3 out of 12 lambs in the low exposure group developed viremia and disease, with similar peak-levels of viremia as the high exposure group but with some heterogeneity in the onset of viremia. These results suggest that the likelihood for successful infection of a ruminant host is affected by the number of infectious mosquitoes biting, but also highlights that a single bite of an infectious mosquito can result in disease. The per bite mosquito-to-host transmission efficiency was estimated at 28% (95% confidence interval: 15 - 47%). We subsequently combined this transmission efficiency with estimates for life traits of Aedes aegypti or related mosquitoes into a Ross-McDonald mathematical model to illustrate scenarios under which major RVFV outbreaks could occur in naïve populations (i.e., R0 >1). The model revealed that relatively high vector-to-host ratios as well as mosquitoes feeding preferably on competent hosts are required for R0 to exceed 1. Altogether, this study highlights the importance of experiments that mimic natural exposure to RVFV. The experiments facilitate a better understanding of the natural progression of disease and a direct way to obtain epidemiological parameters for mathematical models.

Evolutionary modelling indicates that mosquito metabolism shapes the life-history strategies of Plasmodium parasites

Within-host survival and between-host transmission are key life-history traits of single-celled malaria parasites. Understanding the evolutionary forces that shape these traits is crucial to predict malaria epidemiology, drug resistance, and virulence. However, very little is known about how Plasmodium parasites adapt to their mosquito vectors. Here, we examine the evolution of the time Plasmodium parasites require to develop within the vector (extrinsic incubation period) with an individual-based model of malaria transmission that includes mosquito metabolism. Specifically, we model the metabolic cascade of resource allocation induced by blood-feeding, as well as the influence of multiple blood meals on parasite development. Our model predicts that successful vector-to-human transmission events are rare, and are caused by long-lived mosquitoes. Importantly, our results show that the life-history strategies of malaria parasites depend on the mosquito's metabolic status. In our model, additional resources provided by multiple blood meals lead to selection for parasites with slow or intermediate developmental time. These results challenge the current assumption that evolution favors fast developing parasites to maximize their chances to complete their within-mosquito life cycle. We propose that the long sporogonic cycle observed for Plasmodium is not a constraint but rather an adaptation to increase transmission potential.

Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology-Based Model

A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent-based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two-fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi-permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

Interactive effects of climate, land use and soil type on Culex pipiens/torrentium abundance

The incidence and risk of mosquito-borne disease outbreaks in Northwestern Europe has increased over the last few decades. Understanding the underlying environmental drivers of mosquito population dynamics helps to adequately assess mosquito-borne disease risk. While previous studies have focussed primarily on the effects of climatic conditions (i.e., temperature and precipitation) and/or local environmental conditions individually, it remains unclear how climatic conditions interact with local environmental factors such as land use and soil type, and how these subsequently affect mosquito abundance. Here, we set out to study the interactive effects of land use, soil type and climatic conditions on the abundance of Culex pipiens/torrentium, highly abundant vectors of West Nile virus and Usutu virus. Mosquitoes were sampled at 14 sites throughout the Netherlands. At each site, weekly mosquito collections were carried out between early July and mid-October 2020 and 2021. To assess the effect of the aforementioned environmental factors, we performed a series of generalized linear mixed models and non-parametric statistical tests. Our results show that mosquito abundance and species richness consistently differ among land use- and soil types, with peri-urban areas with peat/clay soils having the highest Cx. pipiens/torrentium abundance and sandy rural areas having the lowest. Furthermore, we observed differences in precipitation-mediated effects on Cx. pipiens/torrentium abundance between (peri-)urban and other land uses and soil types. In contrast, effects of temperature on Cx. pipiens/torrentium abundance remain similar between different land use and soil types. Our study highlights the importance of both land use and soil type in conjunction with climatic conditions for understanding mosquito abundances. Particularly in relation to rainfall events, land use and soil type has a marked effect on mosquito abundance. These findings underscore the importance of local environmental parameters for studies focusing on predicting or mitigating disease risk.

Modelling outbreak response impact in human vaccine-preventable diseases: A systematic review of differences in practices between collaboration types before COVID-19

BACKGROUND

Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination.

METHODS

We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices.

RESULTS

Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013-2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation.

CONCLUSION

The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.

Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015-2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models (random forest and neural network), we identified the relative importance and variation in ranking of predictors (historical WNND cases, climate anomalies, human demographics, and land use) across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non-significant differences in performance relative to the baseline model (negative binomial model fit per hexagon). No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines.

Conceptual risk assessment of mosquito population modification gene-drive systems to control malaria transmission: preliminary hazards list workshops

The field-testing and eventual adoption of genetically-engineered mosquitoes (GEMs) to control vector-borne pathogen transmission will require them meeting safety criteria specified by regulatory authorities in regions where the technology is being considered for use and other locales that might be impacted. Preliminary risk considerations by researchers and developers may be useful for planning the baseline data collection and field research used to address the anticipated safety concerns. Part of this process is to identify potential hazards (defined as the inherent ability of an entity to cause harm) and their harms, and then chart the pathways to harm and evaluate their probability as part of a risk assessment. The University of California Malaria Initiative (UCMI) participated in a series of workshops held to identify potential hazards specific to mosquito population modification strains carrying gene-drive systems coupled to anti-parasite effector genes and their use in a hypothetical island field trial. The hazards identified were placed within the broader context of previous efforts discussed in the scientific literature. Five risk areas were considered i) pathogens, infections and diseases, and the impacts of GEMs on human and animal health, ii) invasiveness and persistence of GEMs, and interactions of GEMs with target organisms, iii) interactions of GEMs with non-target organisms including horizontal gene transfer, iv) impacts of techniques used for the management of GEMs and v) evolutionary and stability considerations. A preliminary hazards list (PHL) was developed and is made available here. This PHL is useful for internal project risk evaluation and is available to regulators at prospective field sites. UCMI project scientists affirm that the subsequent processes associated with the comprehensive risk assessment for the application of this technology should be driven by the stakeholders at the proposed field site and areas that could be affected by this intervention strategy.

Effectiveness of Systemic Insecticide Dog Treatment for the Control of Chagas Disease in the Tropics

Chagas disease, caused by Trypanosoma cruzi and transmitted by triatomines, can lead to severe cardiac issues and mortality in many mammals. Recent studies have shown that systemic insecticide treatment of dogs is highly effective in killing triatomines. Here, we assessed the impact of dog treatment on T. cruzi transmission. We developed a mathematical model of T. cruzi transmission among triatomines, dogs, humans, and rodents. We used the model to evaluate the impact of dog treatment regimens on T. cruzi transmission dynamics to determine their effectiveness in reducing T. cruzi infection among hosts. We show that a 3-month treatment regimen may reduce T. cruzi incidence among humans by 59-80% in a high transmission setting, and 26-82% in a low transmission setting. An annual treatment may reduce incidence among humans by 49-74% in a high transmission setting, and by 11-76% in a low transmission setting. However, dog treatment may substantially increase T. cruzi prevalence among dogs if dog consumption of dead triatomines increases. Our model indicates that dog treatment may reduce T. cruzi infections among humans, but it may increase infections in dogs. Therefore, a holistic approach targeting different hosts is necessary for Chagas elimination.

Integrating Community Ecology into Models of Vector-Borne Virus Transmission

Vector-borne plant viruses are a diverse and dynamic threat to agriculture with hundreds of economically damaging viruses and insect vector species. Mathematical models have greatly increased our understanding of how alterations of vector life history and host-vector-pathogen interactions can affect virus transmission. However, insect vectors also interact with species such as predators and competitors in food webs, and these interactions affect vector population size and behaviors in ways that mediate virus transmission. Studies assessing how species' interactions affect vector-borne pathogen transmission are limited in both number and scale, hampering the development of models that appropriately capture community-level effects on virus prevalence. Here, we review vector traits and community factors that affect virus transmission, explore the existing models of vector-borne virus transmission and areas where the principles of community ecology could improve the models and management, and finally evaluate virus transmission in agricultural systems. We conclude that models have expanded our understanding of disease dynamics through simulations of transmission but are limited in their ability to reflect the complexity of ecological interactions in real systems. We also document a need for experiments in agroecosystems, where the high availability of historical and remote-sensing data could serve to validate and improve vector-borne virus transmission models.

HM, Brady OJ, Guerra CA, García GA, Carter AR, Ferguson HM, Afolabi BE, Hay SI, Reiner RC, Kiware S, Smith DL. Spatial dynamics of malaria transmission

The Ross-Macdonald model has exerted enormous influence over the study of malaria transmission dynamics and control, but it lacked features to describe parasite dispersal, travel, and other important aspects of heterogeneous transmission. Here, we present a patch-based differential equation modeling framework that extends the Ross-Macdonald model with sufficient skill and complexity to support planning, monitoring and evaluation for Plasmodium falciparum malaria control. We designed a generic interface for building structured, spatial models of malaria transmission based on a new algorithm for mosquito blood feeding. We developed new algorithms to simulate adult mosquito demography, dispersal, and egg laying in response to resource availability. The core dynamical components describing mosquito ecology and malaria transmission were decomposed, redesigned and reassembled into a modular framework. Structural elements in the framework-human population strata, patches, and aquatic habitats-interact through a flexible design that facilitates construction of ensembles of models with scalable complexity to support robust analytics for malaria policy and adaptive malaria control. We propose updated definitions for the human biting rate and entomological inoculation rates. We present new formulas to describe parasite dispersal and spatial dynamics under steady state conditions, including the human biting rates, parasite dispersal, the "vectorial capacity matrix," a human transmitting capacity distribution matrix, and threshold conditions. An [Formula: see text] package that implements the framework, solves the differential equations, and computes spatial metrics for models developed in this framework has been developed. Development of the model and metrics have focused on malaria, but since the framework is modular, the same ideas and software can be applied to other mosquito-borne pathogen systems.

Biology, bionomics and life-table studies of Anopheles stephensi (Diptera: Culicidae) in Sri Lanka and estimating the vectorial potential using mathematical approximations

BACKGROUND

Anopheles stephensi is an invasive mosquito in Sri Lanka that can potentially transmit malaria. The transmission intensity is linked with biology, bionomic and behavioral aspects of a vector that are associated with the Vectorial Capacity (VC). However, the influence of larval conditions eventually affects the vectorial potential of An. stephensi are not well understood.

METHODS

A colony of An. stephensi was established at the Regional Centre of the Open University of Sri Lanka, Jaffna District. The colony was maintained under confined conditions according to standard protocols. Biotypes of An. stephensi were characterized by referring to the number of egg ridges. Information on (a) biological aspects of eggs (duration for egg hatching, egg development and hatchability), (b) larval development time, larval survivorship pupation success, resting depth of larvae), (c) pupae (adult emergence rate, average time for adult emergence) and (d) adults (biting frequency, mating success gonotrophic cycle, fecundity, duration for egg-laying, percentage of sexes, adult survival/longevity) were evaluated under life-table analysis. Further, selected morphometric characters of each life cycle stage were recorded from the eggs (length and breadth), larvae (head length, width of head, length of thorax, width of thorax, length of abdomen, width of abdomen, and the total length of larvae), pupae (cephalothoracic length and width) and adults (length & width of wing, thorax and abdomen). The VC was calculated using a mathematical-based approach. Descriptive statistics, General Linear Model (GLM) and independent-sample t-test were used for the statistical analysis.

RESULTS

All three biotypes were identified based on egg morphology. Mysorensis biotype (47%; n = 470) was predominant followed by type (38.1%; n = 381) and intermediate (14.9%; n = 149). The mean egg length (F_(2,997) = 3.56; P = 0.029) and breadth (F_(2,997) = 4.57; P = 0.011) denoted significant differences among the three biotypes. The mating success of females observed was 80.7 ± 4.45%. The mean hatching period was 1.9 ± 0.03 days, with a hatching rate of 86.2 ± 0.77%. Overall, 8.0 ± 0.14 days were required for larval development and 30.3 ± 0.14 h were spent in the pupal stage. The pupation success was 94.5 ± 0.37%, and the majority were males (53.1 ± 0.73%). The mean fecundity was 106.5 ± 6.38 eggs and a gonotrophic cycle of 3.4 ± 0.06 days. The female survival rate was 43.2 ± 2.4%, with a mean biting frequency of 66.6 ± 3.5%. The average VC of adult An. stephensi was estimated to be 18.7.

CONCLUSIONS

The type biotype, which is an effective vector in the Indian subcontinent is present in Sri Lanka. According to the mathematical approximation, An. stephensi found locally has a vectorial capacity of over 18. Therefore, this study warrants the health authorities and vector control programmes to continue the entomological surveys, monitoring of vector densities and implementing appropriate vector control interventions based on biology and bionomic information of vectors.

A Scoping Review of Mathematical Models Used to Investigate the Role of Dogs in Chagas Disease Transmission

Chagas disease is a zoonotic vector-borne disease caused by the parasite Trypanosoma cruzi, which affects a variety of mammalian species across the Americas, including humans and dogs. Mathematical modeling has been widely used to investigate the transmission dynamics and control of vector-borne diseases. We performed a scoping review of mathematical models that investigated the role of dogs in T. cruzi transmission. We identified ten peer-reviewed papers that have explicitly modeled the role of dogs in Chagas transmission dynamics. We discuss the different methods employed in these studies, the different transmission metrics, disease transmission routes, and disease control strategies that have been considered and evaluated. In general, mathematical modeling studies have shown that dogs are not only at high risk of T. cruzi infection but are also major contributors to T. cruzi transmission to humans. Moreover, eliminating infected dogs from households or frequent use of insecticide was shown to be effective for curtailing T. cruzi transmission in both humans and dogs. However, when insecticide spraying is discontinued, T. cruzi infections in dogs were shown to return to their pre-spraying levels. We discuss the challenges and opportunities for future modeling studies to improve our understanding of Chagas disease transmission dynamics and control.

Promiscuous feeding on multiple adult honey bee hosts amplifies the vectorial capacity of Varroa destructor

Varroa destructor is a cosmopolitan pest and leading cause of colony loss of the European honey bee. Historically described as a competent vector of honey bee viruses, this arthropod vector is the cause of a global pandemic of Deformed wing virus, now endemic in honeybee populations in all Varroa-infested regions. Our work shows that viral spread is driven by Varroa actively switching from one adult bee to another as they feed. Assays using fluorescent microspheres were used to indicate the movement of fluids in both directions between host and vector when Varroa feed. Therefore, Varroa could be in either an infectious or naïve state dependent upon the disease status of their host. We tested this and confirmed that the relative risk of a Varroa feeding depended on their previous host's infectiousness. Varroa exhibit remarkable heterogeneity in their host-switching behavior, with some Varroa infrequently switching while others switch at least daily. As a result, relatively few of the most active Varroa parasitize the majority of bees. This multiple-feeding behavior has analogs in vectorial capacity models of other systems, where promiscuous feeding by individual vectors is a leading driver of vectorial capacity. We propose that the honeybee-Varroa relationship offers a unique opportunity to apply principles of vectorial capacity to a social organism, as virus transmission is both vectored and occurs through multiple host-to-host routes common to a crowded society.

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

BACKGROUND

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement.

METHODS

We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill.

RESULTS

Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill.

CONCLUSIONS

Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).

SIR-SI model with a Gaussian transmission rate: Understanding the dynamics of dengue outbreaks in Lima, Peru

INTRODUCTION

Dengue is transmitted by the Aedes aegypti mosquito as a vector, and a recent outbreak was reported in several districts of Lima, Peru. We conducted a modeling study to explain the transmission dynamics of dengue in three of these districts according to the demographics and climatology.

METHODOLOGY

We used the weekly distribution of dengue cases in the Comas, Lurigancho, and Puente Piedra districts, as well as the temperature data to investigate the transmission dynamics. We used maximum likelihood minimization and the human susceptible-infected-recovered and vector susceptible-infected (SIR-SI) model with a Gaussian function for the infectious rate to consider external non-modeled variables.

RESULTS/PRINCIPAL FINDINGS

We found that the adjusted SIR-SI model with the Gaussian transmission rate (for modelling the exogenous variables) captured the behavior of the dengue outbreak in the selected districts. The model explained that the transmission behavior had a strong dependence on the weather, cultural, and demographic variables while other variables determined the start of the outbreak.

CONCLUSION/SIGNIFICANCE

The experimental results showed good agreement with the data and model results when a Bayesian-Gaussian transmission rate was employed. The effect of weather was also observed, and a strong qualitative relationship was obtained between the transmission rate and computed effective reproduction number Rt.

Modeling the spread of the Zika virus by sexual and mosquito transmission

Zika Virus (ZIKV) is a flavivirus that is transmitted predominantly by the Aedes species of mosquito, but also through sexual contact, blood transfusions, and congenitally from mother to child. Although approximately 80% of ZIKV infections are asymptomatic and typical symptoms are mild, multiple studies have demonstrated a causal link between ZIKV and severe diseases such as Microcephaly and Guillain Barré Syndrome. Two goals of this study are to improve ZIKV models by considering the spread dynamics of ZIKV as both a vector-borne and sexually transmitted disease, and also to approximate the degree of under-reporting. In order to accomplish these objectives, we propose a compartmental model that allows for the analysis of spread dynamics as both a vector-borne and sexually transmitted disease, and fit it to the ZIKV incidence reported to the National System of Public Health Surveillance in 27 municipalities of Colombia between January 1 2015 and December 31 2017. We demonstrate that our model can represent the infection patterns over this time period with high confidence. In addition, we argue that the degree of under-reporting is also well estimated. Using the model we assess potential viability of public health scenarios for mitigating disease spread and find that targeting the sexual pathway alone has negligible impact on overall spread, but if the proportion of risky sexual behavior increases then it may become important. Targeting mosquitoes remains the best approach of those considered. These results may be useful for public health organizations and governments to construct and implement suitable health policies and reduce the impact of the Zika outbreaks.

A critical assessment of the detailed Aedes aegypti simulation model Skeeter Buster 2 using field experiments of indoor insecticidal control in Iquitos, Peru

The importance of mosquitoes in human pathogen transmission has motivated major research efforts into mosquito biology in pursuit of more effective vector control measures. Aedes aegypti is a particular concern in tropical urban areas, where it is the primary vector of numerous flaviviruses, including the yellow fever, Zika, and dengue viruses. With an anthropophilic habit, Ae. aegypti prefers houses, human blood meals, and ovipositioning in water-filled containers. We hypothesized that this relatively simple ecological niche should allow us to predict the impacts of insecticidal control measures on mosquito populations. To do this, we use Skeeter Buster 2 (SB2), a stochastic, spatially explicit, mechanistic model of Ae. aegypti population biology. SB2 builds on Skeeter Buster, which reproduced equilibrium dynamics of Ae. aegypti in Iquitos, Peru. Our goal was to validate SB2 by predicting the response of mosquito populations to perturbations by indoor insecticidal spraying and widespread destructive insect surveys. To evaluate SB2, we conducted two field experiments in Iquitos, Peru: a smaller pilot study in 2013 (S-2013) followed by a larger experiment in 2014 (L-2014). Here, we compare model predictions with (previously reported) empirical results from these experiments. In both simulated and empirical populations, repeated spraying yielded substantial yet temporary reductions in adult densities. The proportional effects of spraying were broadly comparable between simulated and empirical results, but we found noteworthy differences. In particular, SB2 consistently over-estimated the proportion of nulliparous females and the proportion of containers holding immature mosquitoes. We also observed less temporal variation in simulated surveys of adult abundance relative to corresponding empirical observations. Our results indicate the presence of ecological heterogeneities or sampling processes not effectively represented by SB2. Although additional empirical research could further improve the accuracy and precision of SB2, our results underscore the importance of non-linear dynamics in the response of Ae. aegypti populations to perturbations, and suggest general limits to the fine-grained predictability of its population dynamics over space and time.

The Influence of Anthropogenic and Environmental Disturbances on Parameter Estimation of a Dengue Transmission Model

Some deterministic models deal with environmental conditions and use parameter estimations to obtain experimental parameters, but they do not consider anthropogenic or environmental disturbances, e.g., chemical control or climatic conditions. Even more, they usually use theoretical or measured in-lab parameters without worrying about uncertainties in initial conditions, parameters, or changes in control inputs. Thus, in this study, we estimate parameters (including chemical control parameters) and confidence contours under uncertainty conditions using data from the municipality of Bello (Colombia) during 2010-2014, which includes two epidemic outbreaks. Our study shows that introducing non-periodic pulse inputs into the mathematical model allows us to: (i) perform parameter estimation by fitting real data of consecutive dengue outbreaks, (ii) highlight the importance of chemical control as a method of vector control, and (iii) reproduce the endemic behavior of dengue. We described a methodology for parameter and sub-contour box estimation under uncertainties and performed reliable simulations showing the behavior of dengue spread in different scenarios.

Which trap is best? Alternatives to outdoor human landing catches for malaria vector surveillance: a meta-analysis

BACKGROUND

Human landing catches (HLC) are an entomological collection technique in which humans are used as attractants to capture medically relevant host-seeking mosquitoes. The use of this method has been a topic of extensive debate for decades mainly due to ethical concerns. Many alternatives to HLC have been proposed; however, no quantitative review and meta-analysis comparing HLC to outdoor alternative trapping methods has been conducted.

METHODS

A total of 58 comparisons across 12 countries were identified. We conducted a meta-analysis comparing the standardized mean difference of Anopheles captured by HLC and alternative traps. To explain heterogeneity, three moderators were chosen for analysis: trap type, location of study, and species captured. A meta-regression was fit to understand how the linear combination of moderators helped in explaining heterogeneity. The possibility of biased results due to publication bias was also explored.

RESULTS

Random-effects meta-analysis showed no statistically significant difference in the mean difference of Anopheles collected. Moderator analysis was conducted to determine the effects of trap type, geographical location of study, and the species of Anopheles captured. On average, tent-based traps captured significantly more Anopheles than outdoor HLC (95% CI: [- .9065, - 0.0544]), alternative traps in Africa captured on average more mosquitoes than outdoor HLC (95% CI: [- 2.8750, - 0.0294]), and alternative traps overall captured significantly more Anopheles gambiae s.l. than outdoor HLC (95% CI: [- 4.4613, - 0.2473]) on average. Meta-regression showed that up to 55.77% of the total heterogeneity found can be explained by a linear combination of the three moderators and the interaction between trap type and species. Subset analysis on An. gambiae s.l. showed that light traps specifically captured on average more of this species than HLC (95% CI: [- 18.3751, - 1.0629]). Publication bias likely exists. With 59.65% of studies reporting p-values less than 0.025, we believe there is an over representation in the literature of results indicating that alternative traps are superior to outdoor HLC.

CONCLUSIONS

Currently, there is no consensus on a single "magic bullet" alternative to outdoor HLC. The diversity of many alternative trap comparisons restricts potential metrics for comparisons to outdoor HLC. Further standardization and specific question-driven trap evaluations that consider target vector species and the vector control landscape are needed to allow for robust meta-analyses with less heterogeneity and to develop data-driven decision-making tools for malaria vector surveillance and control.

Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks

We examine how spatial heterogeneity combines with mobility network structure to influence vector-borne disease dynamics. Specifically, we consider a Ross-Macdonald-type disease model on n spatial locations that are coupled by host movement on a strongly connected, weighted, directed graph. We derive a closed form approximation to the domain reproduction number using a Laurent series expansion, and use this approximation to compute sensitivities of the basic reproduction number to model parameters. To illustrate how these results can be used to help inform mitigation strategies, as a case study we apply these results to malaria dynamics in Namibia, using published cell phone data and estimates for local disease transmission. Our analytical results are particularly useful for understanding drivers of transmission when mobility sinks and transmission hot spots do not coincide.

Review of Importance of Weather and Environmental Variables in Agent-Based Arbovirus Models

The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon.

Mechanistic models of Rift Valley fever virus transmission: A systematic review

Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.

Omitting age-dependent mosquito mortality in malaria models underestimates the effectiveness of insecticide-treated nets

Mathematical models of vector-borne infections, including malaria, often assume age-independent mortality rates of vectors, despite evidence that many insects senesce. In this study we present survival data on insecticide-resistant Anopheles gambiae s.l. from experiments in Côte d’Ivoire. We fit a constant mortality function and two age-dependent functions (logistic and Gompertz) to the data from mosquitoes exposed (treated) and not exposed (control) to insecticide-treated nets (ITNs), to establish biologically realistic survival functions. This enables us to explore the effects of insecticide exposure on mosquito mortality rates, and the extent to which insecticide resistance might impact the effectiveness of ITNs. We investigate this by calculating the expected number of infectious bites a mosquito will take in its lifetime, and by extension the vectorial capacity. Our results show that the predicted vectorial capacity is substantially lower in mosquitoes exposed to ITNs, despite the mosquitoes in the experiment being highly insecticide-resistant. The more realistic age-dependent functions provide a better fit to the experimental data compared to a constant mortality function and, hence, influence the predicted impact of ITNs on malaria transmission potential. In models with age-independent mortality, there is a great reduction for the vectorial capacity under exposure compared to no exposure. However, the two age-dependent functions predicted an even larger reduction due to exposure, highlighting the impact of incorporating age in the mortality rates. These results further show that multiple exposures to ITNs had a considerable effect on the vectorial capacity. Overall, the study highlights the importance of including age dependency in mathematical models of vector-borne disease transmission and in fully understanding the impact of interventions.

Interventions against malaria are most commonly targeted on the adult mosquitoes, which transmit the infection from person to person. One of the most important interventions are bed-nets, treated with insecticides. Unfortunately, extensive exposure of mosquitoes to insecticide has led to widespread evolution of insecticide resistance, which might threaten control strategies. Piecing together the overall impact of resistance on the efficacy of insecticide-treated nets is complex, but can be informed by the use of mathematical models. However, there are some assumptions that the models frequently use which are not realistic in terms of the mosquito biology. In this paper, we formulate a model that includes age-dependent mortality rates, an important parameter in vector control since control strategies most commonly aim to reduce the lifespan of the mosquitoes. By using novel data collected using field-derived insecticide-resistant mosquitoes, we explore the effects that the presence of insecticides on nets have on the mortality rates, as well as the difference incorporating age dependency in the model has on the results. We find that including age-dependent mortality greatly alters the anticipated effects of insecticide-treated nets on mosquito transmission potential, and that ignoring this realism potentially overestimates the negative impact of insecticide resistance.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica

Objective.

To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases.

Methods.

Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making.

Results.

The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations.

Conclusions.

Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

Model-Based Projection of Zika Infection Risk with Temperature Effect: A Case Study in Southeast Asia

Zika virus (ZIKV) recently reemerged in the Americas and rapidly expanded in global range. It is posing significant concerns of public health due to its link to birth defects and its complicated transmission routes. Southeast Asia is badly hit by ZIKV, but limited information was found on the transmission potential of ZIKV in the region. In this paper, we develop a new dynamic process-based mathematical model, which incorporates the interactions among humans (sexual transmissibility), and between human and mosquitoes (biting transmissibility), as well as the essential impacts of temperature. The model is first validated by fitting the 2016 ZIKV outbreak in Singapore via Markov chain Monte Carlo method. Based on that, we demonstrate the effects of temperature on mosquito ecology and ZIKV transmission, and further clarify the potential risk of ZIKV outbreak in Southeast Asian countries. The results show that (i) the estimated infection reproduction number [Formula: see text] in Singapore fell from 6.93 (in which the contribution of sexual transmission was 0.89) to 0.24 after the deployment of control strategies; (ii) the optimal temperature for the reproduction of ZIKV infections and adult mosquitoes are estimated to be [Formula: see text]C and [Formula: see text]C, respectively; and (iii) the [Formula: see text] in Southeast Asia could be between 3 and 7, with an inverted-U shape around the year. The large values of [Formula: see text] and the simulative patterns of ZIKV transmission in each country highlights the high risk of ZIKV attack in Southeast Asia.

Relating Eulerian and Lagrangian spatial models for vector-host disease dynamics through a fundamental matrix

We explore the relationship between Eulerian and Lagrangian approaches for modeling movement in vector-borne diseases for discrete space. In the Eulerian approach we account for the movement of hosts explicitly through movement rates captured by a graph Laplacian matrix L. In the Lagrangian approach we only account for the proportion of time that individuals spend in foreign patches through a mixing matrix P. We establish a relationship between an Eulerian model and a Lagrangian model for the hosts in terms of the matrices L and P. We say that the two modeling frameworks are consistent if for a given matrix P, the matrix L can be chosen so that the residence times of the matrix P and the matrix L match. We find a sufficient condition for consistency, and examine disease quantities such as the final outbreak size and basic reproduction number in both the consistent and inconsistent cases. In the special case of a two-patch model, we observe how similar values for the basic reproduction number and final outbreak size can occur even in the inconsistent case. However, there are scenarios where the final sizes in both approaches can significantly differ by means of the relationship we propose.

Competition and resource depletion shape the thermal response of population fitness in Aedes aegypti

Mathematical models that incorporate the temperature dependence of lab-measured life history traits are increasingly being used to predict how climatic warming will affect ectotherms, including disease vectors and other arthropods. These temperature-trait relationships are typically measured under laboratory conditions that ignore how conspecific competition in depleting resource environments—a commonly occurring scenario in nature—regulates natural populations. Here, we used laboratory experiments on the mosquito Aedes aegypti, combined with a stage-structured population model, to investigate this issue. We find that intensified larval competition in ecologically-realistic depleting resource environments can significantly diminish the vector’s maximal population-level fitness across the entire temperature range, cause a ~6 °C decrease in the optimal temperature for fitness, and contract its thermal niche width by ~10 °C. Our results provide evidence for the importance of considering intra-specific competition under depleting resources when predicting how arthropod populations will respond to climatic warming.

Huxley et al. use laboratory experiments to examine how environmental resource depletion impacts temperature-dependent traits observed in Aedes aegypti mosquitoes. The authors find that the conspecific competition dynamics of larvae significantly alter how the mosquito’s population-level fitness responds to temperature, shedding light on how arthropods and other disease vectors may respond to environmental change.

Both consumptive and non-consumptive effects of predators impact mosquito populations and have implications for disease transmission

Predator-prey interactions influence prey traits through both consumptive and non-consumptive effects, and variation in these traits can shape vector-borne disease dynamics. Meta-analysis methods were employed to generate predation effect sizes by different categories of predators and mosquito prey. This analysis showed that multiple families of aquatic predators are effective in consumptively reducing mosquito survival, and that the survival of Aedes, Anopheles, and Culex mosquitoes is negatively impacted by consumptive effects of predators. Mosquito larval size was found to play a more important role in explaining the heterogeneity of consumptive effects from predators than mosquito genus. Mosquito survival and body size were reduced by non-consumptive effects of predators, but development time was not significantly impacted. In addition, Culex vectors demonstrated predator avoidance behavior during oviposition. The results of this meta-analysis suggest that predators limit disease transmission by reducing both vector survival and vector size, and that associations between drought and human West Nile virus cases could be driven by the vector behavior of predator avoidance during oviposition. These findings are likely to be useful to infectious disease modelers who rely on vector traits as predictors of transmission.

Review of the ecology and behaviour of Aedes aegypti and Aedes albopictus in Western Africa and implications for vector control

Western Africa is vulnerable to arboviral disease transmission, having recently experienced major outbreaks of chikungunya, dengue, yellow fever and Zika. However, there have been relatively few studies on the natural history of the two major human arbovirus vectors in this region, Aedes aegypti and Ae. albopictus, potentially limiting the implementation of effective vector control. We systematically searched for and reviewed relevant studies on the behaviour and ecology of Ae. aegypti and Ae. albopictus in Western Africa, published over the last 40 years. We identified 73 relevant studies, over half of which were conducted in Nigeria, Senegal, or Côte d'Ivoire. Most studies investigated the ecology of Ae. aegypti and Ae. albopictus, exploring the impact of seasonality and land cover on mosquito populations and identifying aquatic habitats. This review highlights the adaptation of Ae. albopictus to urban environments and its invasive potential, and the year-round maintenance of Ae. aegypti populations in water storage containers. However, important gaps were identified in the literature on the behaviour of both species, particularly Ae. albopictus. In Western Africa, Ae. aegypti and Ae. albopictus appear to be mainly anthropophilic and to bite predominantly during the day, but further research is needed to confirm this to inform planning of effective vector control strategies. We discuss the public health implications of these findings and comment on the suitability of existing and novel options for control in Western Africa.

Advances in Understanding Vector Behavioural Traits after Infection

Vector behavioural traits, such as fitness, host-seeking, and host-feeding, are key determinants of vectorial capacity, pathogen transmission, and epidemiology of the vector-borne disease. Several studies have shown that infection with pathogens can alter these behavioural traits of the arthropod vector. Here, we review relevant publications to assess how pathogens modulate the behaviour of mosquitoes and ticks, major vectors for human diseases. The research has shown that infection with pathogens alter the mosquito’s flight activity, mating, fecundity, host-seeking, blood-feeding, and adaptations to insecticide bed nets, and similarly modify the tick’s locomotion, questing heights, vertical and horizontal walks, tendency to overcome obstacles, and host-seeking ability. Although some of these behavioural changes may theoretically increase transmission potential of the pathogens, their effect on the disease epidemiology remains to be verified. This study will not only help in understanding virus–vector interactions but will also benefit in establishing role of these behavioural changes in improved epidemiological models and in devising new vector management strategies.

Microbiota and transcriptome changes of Culex pipiens pallens larvae exposed to Bacillus thuringiensis israelensis

Culex pipiens pallens is an important vector of lymphatic filariasis and epidemic encephalitis. Mosquito control is the main strategy used for the prevention of mosquito-borne diseases. Bacillus thuringiensis israelensis (Bti) is an entomopathogenic bacterium widely used in mosquito control. In this study, we profiled the microbiota and transcriptional response of the larvae of Cx. pipiens pallens exposed to different concentrations of Bti. The results demonstrated that Bti induced a significant effect on both the microbiota and gene expression of Cx. pipiens pallens. Compared to the control group, the predominant bacteria changed from Actinobacteria to Firmicutes, and with increase in the concentration of Bti, the abundance of Actinobacteria was gradually reduced. Similar changes were also detected at the genus level, where Bacillus replaced Microbacterium, becoming the predominant genus in Bti-exposed groups. Furthermore, alpha diversity analysis indicated that Bti exposure changed the diversity of the microbota, possibly because the dysbiosis caused by the Bti infection inhibits some bacteria and provides opportunities to other opportunistic taxa. Pathway analysis revealed significant enhancement for processes associated with sphingolipid metabolism, glutathione metabolism and glycerophospholipid metabolism between all Bti-exposed groups and control group. Additionally, genes associated with the Toll and Imd signaling pathway were found to be notably upregulated. Bti infection significantly changed the bacterial community of larvae of Cx. pipiens pallens.

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m-km, days-weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

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.

Transmission modelling of environmentally persistent zoonotic diseases: a systematic review

Transmission of many infectious diseases depends on interactions between humans, animals, and the environment. Incorporating these complex processes in transmission dynamic models can help inform policy and disease control interventions. We identified 20 diseases involving environmentally persistent pathogens (ie, pathogens that survive for more than 48 h in the environment and can cause subsequent human infections), of which indirect transmission can occur from animals to humans via the environment. Using a systematic approach, we critically appraised dynamic transmission models for environmentally persistent zoonotic diseases to quantify traits of models across diseases. 210 transmission modelling studies were identified and most studies considered diseases of domestic animals or high-income settings, or both. We found that less than half of studies validated their models to real-world data, and environmental data on pathogen persistence was rarely incorporated. Model structures varied, with few studies considering the animal-human-environment interface of transmission in the context of a One Health framework. This Review highlights the need for more data-driven modelling of these diseases and a holistic One Health approach to model these pathogens to inform disease prevention and control strategies.

A novel approach for predicting risk of vector-borne disease establishment in marginal temperate environments under climate change: West Nile virus in the UK

Vector-borne diseases (VBDs), such as dengue, Zika, West Nile virus (WNV) and tick-borne encephalitis, account for substantial human morbidity worldwide and have expanded their range into temperate regions in recent decades. Climate change has been proposed as a likely driver of past and future expansion, however, the complex ecology of host and vector populations and their interactions with each other, environmental variables and land-use changes makes understanding the likely impacts of climate change on VBDs challenging. We present an environmentally driven, stage-structured, host-vector mathematical modelling framework to address this challenge. We apply our framework to predict the risk of WNV outbreaks in current and future UK climates. WNV is a mosquito-borne arbovirus which has expanded its range in mainland Europe in recent years. We predict that, while risks will remain low in the coming two to three decades, the risk of WNV outbreaks in the UK will increase with projected temperature rises and outbreaks appear plausible in the latter half of this century. This risk will increase substantially if increased temperatures lead to increases in the length of the mosquito biting season or if European strains show higher replication at lower temperatures than North American strains.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Increasing Effectiveness of Genetically Modifying Mosquito Populations: Risk Assessment of Releasing Blood-Fed Females

Releasing mosquito refractory to pathogens has been proposed as a means of controlling mosquito-borne diseases. A recent modeling study demonstrated that instead of the conventional male-only releases, adding blood-fed females to the release population could significantly increase the program's efficiency, hastening the decrease in disease transmission competence of the target mosquito population and reducing the duration and costs of the release program. However, releasing female mosquitoes presents a short-term risk of increased disease transmission. To quantify this risk, we constructed a Ross-MacDonald model and an individual-based stochastic model to estimate the increase in disease transmission contributed by the released blood-fed females, using the mosquito Aedes aegypti and the dengue virus as a model system. Under baseline parameter values informed by empirical data, our stochastic models predicted a 1.1-5.5% increase in dengue transmission during the initial release, depending on the resistance level of released mosquitoes and release size. The basic reproductive number (R0) increased by 0.45-3.62%. The stochastic simulations were then extended to 10 releases to evaluate the long-term effect. The overall reduction of disease transmission was much greater than the number of potential infections directly contributed by the released females. Releasing blood-fed females with males could also outperform conventional male-only releases when the release strain is sufficiently resistant, and the release size is relatively small. Overall, these results suggested that the long-term benefit of releasing blood-fed females often outweighs the short-term risk.

Quantifying the risk of vector-borne disease transmission attributable to genetically modified vectors

The relative risk of disease transmission caused by the potential release of transgenic vectors, such as through sterile insect technique or gene drive systems, is assessed with comparison with wild-type vectors. The probabilistic risk framework is demonstrated with an assessment of the relative risk of lymphatic filariasis, malaria and o'nyong'nyong arbovirus transmission by mosquito vectors to human hosts given a released transgenic strain of Anopheles coluzzii carrying a dominant sterile male gene construct. Harm is quantified by a logarithmic loss function that depends on the causal risk ratio, which is a quotient of basic reproduction numbers derived from mathematical models of disease transmission. The basic reproduction numbers are predicted to depend on the number of generations in an insectary colony and the number of backcrosses between the transgenic and wild-type lineages. Analogous causal risk ratios for short-term exposure to a single cohort release are also derived. These causal risk ratios were parametrized by probabilistic elicitations, and updated with experimental data for adult vector mortality. For the wild-type, high numbers of insectary generations were predicted to reduce the number of infectious human cases compared with uncolonized wild-type. Transgenic strains were predicted to produce fewer infectious cases compared with the uncolonized wild-type.