PLoS One. 2017 Mar 31;12(3):e0174293.

A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology 

Kamil Erguler1,*, Nastassya L Chandra2, Yiannis Proestos1, Jos Lelieveld3,1, George K Christophides4,5, Paul E Parham6,*

[1] Energy, Environment and Water Research Center, The Cyprus Institute, 2121 Aglantzia, Nicosia, Cyprus

[2] Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, London W2 1PG, UK

[3] Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, D-55128 Mainz, Germany

[4] Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

[5] Computation-based Science and Technology Research Center, The Cyprus Institute, 2121 Aglantzia, Nicosia, Cyprus

[6] Department of Public Health and Policy, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 3GL, UK

Correspondence should be addressed to Kamil Erguler, Energy, Environment and Water Research Center, The Cyprus Institute, 2121 Aglantzia, Nicosia, Cyprus (e-mail:, or Paul E Parham, Department of Public Health and Policy, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 3GL, UK (e-mail:



Chikungunya is a viral disease transmitted to humans primarily via the bites of infected Aedes mosquitoes. The virus caused a major epidemic in the Indian Ocean in 2004, affecting millions of inhabitants, while cases have also been observed in Europe since 2007. We developed a stochastic spatiotemporal model of Aedes albopictus-borne chikungunya transmission based on our recently developed environmentally-driven vector population dynamics model. We designed an integrated modelling framework incorporating large-scale gridded climate datasets to investigate disease outbreaks on Reunion Island and in Italy. We performed Bayesian parameter inference on the surveillance data, and investigated the validity and applicability of the underlying biological assumptions. The model successfully represents the outbreak and measures of containment in Italy, suggesting wider applicability in Europe. In its current configuration, the model implies two different viral strains, thus two different outbreaks, for the two-stage Reunion Island epidemic. Characterisation of the posterior distributions indicates a possible relationship between the second larger outbreak on Reunion Island and the Italian outbreak. The model suggests that vector control measures, with different modes of operation, are most effective when applied in combination: adult vector intervention has a high impact but is short-lived, larval intervention has a low impact but is long-lasting, and quarantining infected territories, if applied strictly, is effective in preventing large epidemics. We present a novel approach in analysing chikungunya outbreaks globally using a single environmentally-driven mathematical model. Our study represents a significant step towards developing a globally applicable Ae. albopictus-borne chikungunya transmission model, and introduces a guideline for extending such models to other vector-borne diseases.


The following text and figures are adapted from the original publication (CC BY:


Chikungunya is an incapacitating disease caused by the recently re-emerging chikungunya virus (CHIKV), which is transmitted to humans via mosquitoes of the Aedes genus [1]. Over the last 10 years, outbreaks occurred in many Asian and African countries, in continental Europe and the Americas [2]. The first reported outbreak of CHIKV in Europe was in Italy in 2007 [3]. CHIKV was identified in South East France in September 2010 [4], and imported cases have also been detected in Germany, Norway, China, and the Philippines [5]. The first local transmission of CHIKV in United States was detected in Caribbean territories in 2013 [6]. Since then, local transmissions have also been detected in Florida, Puerto Rico, and the U.S. Virgin Islands [6].


Ae. albopictus can successfully inhabit both tropical and temperate climates. Its wide geographic distribution arises from its ability to inhabit both rural and urban settings and display both anthropophilic and zoophilic behaviour [7]. The global distribution and population dynamics of Ae. albopictus are highly influenced by environmental variables [8, 9]; temperature significantly affects the mosquito developmental and metabolic rates, while it is thought to also influence egg production and the frequency of blood meals. Rainfall is tightly associated to breeding sites of aquatic immature mosquito stages; while rainfall is necessary for feeding and development, heavy rainfall compromises breeding sites by sweeping away the immature stages.


Here, we employed our environmentally-driven population dynamics model of Ae. albopictus, which has been validated on the Emilia-Romagna region of Italy and demonstrated reasonably good predictive capacity across Europe [10]. We developed a discrete-time stochastic susceptible / exposed / infected / recovered / chronic-stage (SEIRC) model for Ae. albopictus-borne chikungunya transmission. We implemented the model in ANSI C and developed a Python interface to run and facilitate integration with various environmental data sets. The code and related material are available in the albopictus Python package (v.0.8). Instead of imposing any estimated CHIKV transmission parameters, we adopted a Bayesian approach to inform the parameters using the outbreak surveillance data. We used the Python (v2.7) implementation of the hoppMCMC (v0.5) algorithm to arrive at a set of parameter configurations, which explains the surveillance data best. The algorithm benefits from a variable acceptance probability to traverse the posterior distribution and to sample the most probable parameter configurations.


The resulting model predicts that the outbreak in Italy could have lasted longer if not for the administration of effective vector control measures [3, 11]. According to the model, the probability for a secondary infection resulting from the imported (index) case was as high as 0.43. In addition, between the time of virus introduction and the predicted end of the outbreak, towards the end of November, the model predicted that the median number of human infections, including both symptomatic and asymptomatic cases, was 1700 (95% CI: 100 – 3900). These predictions correspond to a high outbreak risk for the combination of location and time of virus introduction. We estimated that both the probability and impact of the outbreak are in agreement with the data and the previous study of Poletti et al. [12] although our inference procedure employed no prior information on parameter values but benefitted only from the observed epidemic trajectory (Figure 1).



Figure 1. Predicted epidemic trajectory of the Italian chikungunya outbreak. Trajectories are predicted with (red) or without (grey) vector control measures (intervention), and compared against the incidence reports (dark circles). Estimated date of introduction of the first infectious human case (the index case) is also shown. Solid lines indicate the median and the shaded areas indicate the 95% range of predicted epidemic trajectories.


We defined two indicators of outbreak severity, outbreak risk and outbreak impact, and assessed the Italian chikungunya outbreak using our model. Outbreak risk, which is similar to the traditional measure R0, is the probability that an infected host gives rise to a secondary infection in a fully susceptible human population. Outbreak impact, on the other hand, is the number of human infections observed during the course of an outbreak. Instead of employing any simplifying assumptions to calculate outbreak severity, we estimated these indicators numerically from the model output.


In Figure 2, we demonstrate the behaviour of each indicator with respect to the time of introduction of the index case. We found that the outbreak risk is higher at the beginning of the mosquito season, then, it drops gradually. The risk decreases earlier than the decline in adult abundance, as it is associated with both vector count and activity. The non-zero outbreak risk during winter months is due to the existence of the chronic stage, often overlooked by traditional modelling approaches; the introduction of an infectious person might trigger a chronic infection, which might relapse anytime during a high-risk period. According to the mission report of the European Centre for Disease Prevention and Control (ECDC) [3], the index case was introduced on 15 June, but the infectious episode resulting in the outbreak was a relapse on 23 June, which makes the modelling of the chronic stage relevant for the Italian chikungunya outbreak.



Figure 2. Risk assessment of Ae. albopictus-borne chikungunya. The median outbreak risk (red line, scale on the right) and the median impact ratio (fraction of total infections, black line, scale on the right) are plotted together with predicted adult abundance (blue line, scale on the left). The x-axis corresponds to the time of introduction of the index case.


While traditional measures pertain to the outbreak risk, the impact of a possible outbreak can be determined by following transmission dynamics in time. The model suggests that the impact is larger if an outbreak takes place at the beginning of the mosquito season. In essence, time-dependent risk assessment of an Ae. albopictus-borne chikungunya outbreak can be categorised into four consecutive stages: low risk-high impact, high risk-high impact, high risk-low impact, and low risk-low impact.


The current study is a step towards a global outbreak model for Ae. albopictus-borne chikungunya, and a foundation for developing widely applicable disease transmission models for other vector-borne diseases by employing surveillance data as the main source of information.



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