A generic weather-driven model to predict mosquito population dynamics applied to species of Anopheles, Culex and Aedes genera of southern France

Spatiotemporally Explicit Epidemic Model for West Nile Virus Outbreak in Germany: An Inversely Calibrated Approach

Since the first autochthonous transmission of West Nile Virus was detected in Germany (WNV) in 2018, it has become endemic in several parts of the country and is continuing to spread due to the attainment of a suitable environment for vector occurrence and pathogen transmission. Increasing temperature associated with a changing climate has been identified as a potential driver of mosquito-borne disease in temperate regions. This scenario justifies the need for the development of a spatially and temporarily explicit model that describes the dynamics of WNV transmission in Germany. In this study, we developed a process-based mechanistic epidemic model driven by environmental and epidemiological data. Functional traits of mosquitoes and birds of interest were used to parameterize our compartmental model appropriately. Air temperature, precipitation, and relative humidity were the key climatic forcings used to replicate the fundamental niche responsible for supporting mosquito population and infection transmission risks in the study area. An inverse calibration method was used to optimize our parameter selection. Our model was able to generate spatially and temporally explicit basic reproductive number (R0) maps showing dynamics of the WNV occurrences across Germany, which was strongly associated with the deviation from daily means of climatic forcings, signaling the impact of a changing climate in vector-borne disease dynamics. Epidemiological data for human infections sourced from Robert Koch Institute and animal cases collected from the Animal Diseases Information System (TSIS) of the Friedrich-Loeffler-Institute were used to validate model-simulated transmission rates. From our results, it was evident that West Nile Virus is likely to spread towards the western parts of Germany with the rapid attainment of environmental suitability for vector mosquitoes and amplifying host birds, especially short-distance migratory birds. Locations with high risk of WNV outbreak (Baden-Württemberg, Bavaria, Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony-Anhalt and Saxony) were shown on R0 maps. This study presents a path for developing an early warning system for vector-borne diseases driven by climate change.

Linking mathematical models and trap data to infer the proliferation, abundance, and control of Aedes aegypti

Aedes aegypti is one of the most dominant mosquito species in the urban areas of Miami-Dade County, Florida, and is responsible for the local arbovirus transmissions. Since August 2016, mosquito traps have been placed throughout the county to improve surveillance and guide mosquito control and arbovirus outbreak response. In this paper, we develop a deterministic mosquito population model, estimate model parameters by using local entomological and temperature data, and use the model to calibrate the mosquito trap data from 2017 to 2019. We further use the model to compare the Ae. aegypti population and evaluate the impact of rainfall intensity in different urban built environments. Our results show that rainfall affects the breeding sites and the abundance of Ae. aegypti more significantly in tourist areas than in residential places. In addition, we apply the model to quantitatively assess the effectiveness of vector control strategies in Miami-Dade County.

Complementarity of empirical and process-based approaches to modelling mosquito population dynamics with Aedes albopictus as an example-Application to the development of an operational mapping tool of vector populations

Mosquitoes are responsible for the transmission of major pathogens worldwide. Modelling their population dynamics and mapping their distribution can contribute effectively to disease surveillance and control systems. Two main approaches are classically used to understand and predict mosquito abundance in space and time, namely empirical (or statistical) and process-based models. In this work, we used both approaches to model the population dynamics in Reunion Island of the 'Tiger mosquito', Aedes albopictus, a vector of dengue and chikungunya viruses, using rainfall and temperature data. We aimed to i) evaluate and compare the two types of models, and ii) develop an operational tool that could be used by public health authorities and vector control services. Our results showed that Ae. albopictus dynamics in Reunion Island are driven by both rainfall and temperature with a non-linear relationship. The predictions of the two approaches were consistent with the observed abundances of Ae. albopictus aquatic stages. An operational tool with a user-friendly interface was developed, allowing the creation of maps of Ae. albopictus densities over the whole territory using meteorological data collected from a network of weather stations. It is now routinely used by the services in charge of vector control in Reunion Island.

Integrated Early Warning Surveillance: Achilles' Heel of One Health?

Emerging and re-emerging infectious diseases and zoonoses indicate the importance of the One Health (OH) approach for early warning. At present, even when surveillance data are available, they are infrequently timeously shared between the health sectors. In the context of the MediLabSecure (MLS) Project, we investigated the collection of a set of surveillance indicators able to provide data for the implementation of integrated early warning systems in the 22 MLS countries of the Mediterranean, Black Sea and Sahel regions. We used an online questionnaire (covering vector, human, and animal sectors), focusing on seven relevant arboviruses, that was submitted to 110 officially appointed experts. Results showed that West Nile virus was perceived as the most relevant zoonotic pathogen, while Dengue virus was the most relevant non-zoonotic pathogen in the study area. Data collection of early warning indicators is in place at a different level for all the investigated pathogens and in almost all the MLS Countries. Further assessments on the reliability of the collection in place and on the feasibility of piloting an integrated early warning system for arbovirus could verify if integrated early warning really represents the Achilles' heel of OH.

Spatial Modeling of Mosquito Vectors for Rift Valley Fever Virus in Northern Senegal: Integrating Satellite-Derived Meteorological Estimates in Population Dynamics Models

Mosquitoes are vectors of major pathogen agents worldwide. Population dynamics models are useful tools to understand and predict mosquito abundances in space and time. To be used as forecasting tools over large areas, such models could benefit from integrating remote sensing data that describe the meteorological and environmental conditions driving mosquito population dynamics. The main objective of this study is to assess a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables. A generic weather-driven model of mosquito population dynamics was applied to Rift Valley fever vector species in northern Senegal, with rainfall, temperature, and humidity as inputs. The model outputs using meteorological data from ground weather station vs satellite-based estimates are compared, using longitudinal mosquito trapping data for validation at local scale in three different ecosystems. Model predictions were consistent with field entomological data on adult abundance, with a better fit between predicted and observed abundances for the Sahelian Ferlo ecosystem, and for the models using in-situ weather data as input. Based on satellite-derived rainfall and temperature data, dynamic maps of three potential Rift Valley fever vector species were then produced at regional scale on a weekly basis. When direct weather measurements are sparse, these resulting maps should be used to support policy-makers in optimizing surveillance and control interventions of Rift Valley fever in Senegal.

Effects of host sex, body mass and infection by avian Plasmodium on the biting rate of two mosquito species with different feeding preferences

BACKGROUND

The transmission of mosquito-borne pathogens is strongly influenced by the contact rates between mosquitoes and susceptible hosts. The biting rates of mosquitoes depend on different factors including the mosquito species and host-related traits (i.e. odour, heat and behaviour). However, host characteristics potentially affecting intraspecific differences in the biting rate of mosquitoes are poorly known. Here, we assessed the impact of three host-related traits on the biting rate of two mosquito species with different feeding preferences: the ornithophilic Culex pipiens and the mammophilic Ochlerotatus (Aedes) caspius. Seventy-two jackdaws Corvus monedula and 101 house sparrows Passer domesticus were individually exposed to mosquito bites to test the effect of host sex, body mass and infection status by the avian malaria parasite Plasmodium on biting rates.

RESULTS

Ochlerotatus caspius showed significantly higher biting rates than Cx. pipiens on jackdaws, but non-significant differences were found on house sparrows. In addition, more Oc. caspius fed on female than on male jackdaws, while no differences were found for Cx. pipiens. The biting rate of mosquitoes on house sparrows increased through the year. The bird infection status and body mass of both avian hosts were not related to the biting rate of both mosquito species.

CONCLUSIONS

Host sex was the only host-related trait potentially affecting the biting rate of mosquitoes, although its effect may differ between mosquito and host species.

Modeling a single season of Aedes albopictus populations based on host-seeking data in response to temperature and precipitation in eastern Tennessee

In the southern Appalachia of the U.S., Aedes mosquitoes maintain and transmit La Crosse virus (LACV) which causes La Crosse encephalitis, a neuroinvasive disease of children. In response to mosquito outbreaks, communities organize prevention, detection, and response measures that are dependent on local characteristics of the mosquito population and the community. Knowing Ae. albopictus is an accessory vector of LACV and a nuisance biter, our objective was to build a system of ordinary differential equations to model dynamics in a single season using our data and readily available environmental variables that can reflect the abundance and activity of Ae. albopictus. Consequently, we built an Ae. albopictus single-season mathematical model for eastern Tennessee to fit our 2013 mosquito collection data in order to understand the population fluctuations. We included precipitation, temperature, and rate of change of temperature in the model because Aedes mosquitoes oviposit desiccant tolerant eggs with peak activity occurring over 26° C and those data are readily available and used frequently as forecast predictors. Our ordinary differential equation model accurately fits the data and facilitates predictions and better understanding of Ae. albopictus populations in southern Appalachia.

Free boundary models for mosquito range movement driven by climate warming

As vectors, mosquitoes transmit numerous mosquito-borne diseases. Among the many factors affecting the distribution and density of mosquitoes, climate change and warming have been increasingly recognized as major ones. In this paper, we make use of three diffusive logistic models with free boundary in one space dimension to explore the impact of climate warming on the movement of mosquito range. First, a general model incorporating temperature change with location and time is introduced. In order to gain insights of the model, a simplified version of the model with the change of temperature depending only on location is analyzed theoretically, for which the dynamical behavior is completely determined and presented. The general model can be modified into a more realistic one of seasonal succession type, to take into account of the seasonal changes of mosquito movements during each year, where the general model applies only for the time period of the warm seasons of the year, and during the cold season, the mosquito range is fixed and the population is assumed to be in a hibernating status. For both the general model and the seasonal succession model, our numerical simulations indicate that the long-time dynamical behavior is qualitatively similar to the simplified model, and the effect of climate warming on the movement of mosquitoes can be easily captured. Moreover, our analysis reveals that hibernating enhances the chances of survival and successful spreading of the mosquitoes, but it slows down the spreading speed.

Population of the temperate mosquito, Culex pipiens, decreases in response to habitat climatological changes in future

Predictions of the temporal distribution of vector mosquitoes are an important issue for human health because the response of mosquito populations to climate change could have implications for the risk of vector-borne diseases. To elucidate the effects of climate change on mosquito populations inhabiting temperate regions, we developed a Physiology-based Climate-driven Mosquito Population model for temperate regions. For accurately reproducing the temporal patterns observed in mosquito populations, the key factors were identified by implementing the combinations of factors into the model. We focused on three factors: the effect of diapause, the positive effect of rainfall on larval carrying capacity, and the negative effect of rainfall as the washout mortality on aquatic stages. For each model, parameters were calibrated using weekly observation data of a Culex pipiens adult population collected in Tokyo, Japan. Based on its likelihood value, the model incorporating diapause, constant carrying capacity, and washout mortality was the best to replicate the observed data. By using the selected model and applying global climate model data, our results indicated that the mosquito population would decrease and adults' active season would be shortened under future climate conditions. We found that incorporating the washout effect in the model settings or not caused a difference in the temporal patterns in the projected mosquito populations. This suggested that water resources in mosquito habitats in temperate regions should be considered for predicting the risk of vector-borne diseases in such regions.

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An important tool widely used is modeling and simulation, which help us to understand epidemic dynamics and create scenarios to support planning and decision making processes. With this aim, we proposed and developed DengueME, a collaborative open source platform to simulate dengue disease and its vector's dynamics. It supports compartmental and individual-based models, implemented over a GIS database, that represent Aedes aegypti population dynamics, human demography, human mobility, urban landscape and dengue transmission mediated by human and mosquito encounters. A user-friendly graphical interface was developed to facilitate model configuration and data input, and a library of models was developed to support teaching-learning activities. DengueME was applied in study cases and evaluated by specialists. Other improvements will be made in future work, to enhance its extensibility and usability.

Rift Valley fever vector diversity and impact of meteorological and environmental factors on Culex pipiens dynamics in the Okavango Delta, Botswana

Background

In Northern Botswana, rural communities, livestock, wildlife and large numbers of mosquitoes cohabitate around permanent waters of the Okavango Delta. As in other regions of sub-Saharan Africa, Rift Valley Fever (RVF) virus is known to circulate in that area among wild and domestic animals. However, the diversity and composition of potential RVF mosquito vectors in that area are unknown as well as the climatic and ecological drivers susceptible to affect their population dynamics.

Methods

Using net traps baited with carbon dioxide, monthly mosquito catches were implemented over four sites surrounding cattle corrals at the northwestern border of the Okavango Delta between 2011 and 2012. The collected mosquito species were identified and analysed for the presence of RVF virus by molecular methods. In addition, a mechanistic model was developed to assess the qualitative influence of meteorological and environmental factors such as temperature, rainfall and flooding levels, on the population dynamics of the most abundant species detected (Culex pipiens).

Results

More than 25,000 mosquitoes from 32 different species were captured with an overabundance of Cx. pipiens (69,39 %), followed by Mansonia uniformis (20,67 %) and a very low detection of Aedes spp. (0.51 %). No RVF virus was detected in our mosquito pooled samples. The model fitted well the Cx. pipiens catching results (ρ = 0.94, P = 0.017). The spatial distribution of its abundance was well represented when using local rainfall and flooding measures (ρ = 1, P = 0.083). The global population dynamics were mainly influenced by temperature, but both rainfall and flooding presented a significant influence. The best and worst suitable periods for mosquito abundance were around March to May and June to October, respectively.

Conclusions

Our study provides the first available data on the presence of potential RVF vectors that could contribute to the maintenance and dissemination of RVF virus in the Okavango Delta. Our model allowed us to understand the dynamics of Cx. pipiens, the most abundant vector identified in this area. Potential predictions of peaks in abundance of this vector could allow the identification of the most suitable periods for disease occurrence and provide recommendations for vectorial and disease surveillance and control strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1712-1) contains supplementary material, which is available to authorized users.

A Stochastic Model to Study Rift Valley Fever Persistence with Different Seasonal Patterns of Vector Abundance: New Insights on the Endemicity in the Tropical Island of Mayotte

Rift Valley fever (RVF) is a zoonotic vector-borne disease causing abortion storms in cattle and human epidemics in Africa. Our aim was to evaluate RVF persistence in a seasonal and isolated population and to apply it to Mayotte Island (Indian Ocean), where the virus was still silently circulating four years after its last known introduction in 2007. We proposed a stochastic model to estimate RVF persistence over several years and under four seasonal patterns of vector abundance. Firstly, the model predicted a wide range of virus spread patterns, from obligate persistence in a constant or tropical environment (without needing vertical transmission or reintroduction) to frequent extinctions in a drier climate. We then identified for each scenario of seasonality the parameters that most influenced prediction variations. Persistence was sensitive to vector lifespan and biting rate in a tropical climate, and to host viraemia duration and vector lifespan in a drier climate. The first epizootic peak was primarily sensitive to viraemia duration and thus likely to be controlled by vaccination, whereas subsequent peaks were sensitive to vector lifespan and biting rate in a tropical climate, and to host birth rate and viraemia duration in arid climates. Finally, we parameterized the model according to Mayotte known environment. Mosquito captures estimated the abundance of eight potential RVF vectors. Review of RVF competence studies on these species allowed adjusting transmission probabilities per bite. Ruminant serological data since 2004 and three new cross-sectional seroprevalence studies are presented. Transmission rates had to be divided by more than five to best fit observed data. Five years after introduction, RVF persisted in more than 10% of the simulations, even under this scenario of low transmission. Hence, active surveillance must be maintained to better understand the risk related to RVF persistence and to prevent new introductions.

One Health surveillance - More than a buzz word?

One Health surveillance describes the systematic collection, validation, analysis, interpretation of data and dissemination of information collected on humans, animals and the environment to inform decisions for more effective, evidence- and system-based health interventions. During the second International Conference on Animal Health Surveillance (ICAHS) in Havana, Cuba, a panel discussion was organised to discuss the relevance of One Health in the context of surveillance. A number of success stories were presented which generally focused on the obvious interfaces between human and veterinary medicine such as zoonoses and food safety. Activities aimed at strengthening inter-sectoral networking through technical collaboration, conferences, workshops and consultations have resulted in recommendations to advance the One Health concept. There are also several One Health educational programmes offered as Masters programmes. Continuing challenges to One Health surveillance were identified at both technical as well as organisational level. It was acknowledged that the public health sector and the environmental sector could be engaged more in One Health activities. Legal issues, hurdles to data sharing, unclear responsibilities and structural barriers between ministries prevent integrated action. Policy makers in the health sector often perceive One Health as a veterinary-driven initiative that is not particularly relevant to their priority problems. Whilst some funding schemes allow for the employment of scientists and technicians for research projects, the development of a sustainable One Health workforce has yet to be broadly demonstrated. Funding opportunities do not explicitly promote the development of One Health surveillance systems. In addition, organisational, legal and administrative barriers may prevent operational implementation. Strategies and communication across sectors need to be aligned. Whilst at the technical or local level the formal separation can be bridged, separate funding sources and budgets can jeopardise the overall strategy, especially if funding cuts are later required. To overcome such challenges, a strong business case for One Health surveillance is needed. This should include the costs and benefits of One Health activities or projects including consequences of different strategies as well as risks. Integrated training should also be further promoted. Future ICAHS conferences should continue to provide a platform for discussing surveillance in the One Health context and to provide a forum for surveillance professionals from all relevant sectors to interact.