# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## 1 Introduction  
The 2015-2016 Zika virus (ZIKV) epidemic in the Americas was one of the largest vector borne disease outbreaks with over a million reported cases. The causal links between ZIKV infection and serious neurological disorders, like microcephaly [1] and Guillan-Barre Syndrome (GBS) [1] have led to a major global concern. The reasons for the virus' higher infectivity and increased severity are being researched extensively. Some studies have partially shown that the enhanced viremia of Zika in humans is a result of a new mutation of the virus acquired after the Micronesia outbreak in 2007 [2; 3]. Zika can be transmitted to humans by infected _Aedes Agypti_ or _Aedes Albopictus_ mosquito bites, sexual contact, or blood transfusions [1]. ZIKV is very similar to the other arboviruses like Dengue Virus (DNV) and West Nile Virus (WNV) in terms of diagnostics, and additionally Zika is asymptomatic in about 80% cases [1]. The asymptomatic nature and difficult differential diagnosisof the disease lead to inconsistent case reporting. This makes predicting and controlling the scale of a Zika epidemic challenging, and thus necessitates computational methods to simulate outbreaks, study the transmission dynamics of the disease, and develop effective intervention strategies. Since Zika is a vector-borne disease, the size of the vector population in the affected area plays a key role in governing the scale of the outbreak. The _Aedes_ mosquitoes are highly sensitive to environmental conditions, and thus temperature and precipitation are critical to mosquito survival and breeding [4]. Additionally, increased international travel is another major factor that has caused the disease outbreak to turn into a pandemic affecting five continents. The 2013-2014 Zika outbreak in French Polynesia was the largest Zika outbreak before the Americas outbreak in 2015 [5]. This study presents an enhanced dynamic model that simulates the transmission of Zika in 23 islands of French Polynesia incorporating a temperature dependent mosquito ecology instead of using a static vector population and the local transit network (flights and ferries) in French Polynesia. Furthermore, the model is used to study the effectiveness of outbreak control and mitigation methods like prevention of mosquito bites and imposition of travel bans after a set number of cases have been reported. The results also highlight the importance of early reporting and effectuation of prevention and control strategies. Markov Chain Monte Carlo experiments were conducted to estimate the size of the outbreak for each configuration of the model. In order to estimate the size of the mosquito population, it is important to model the mosquito life cycle which includes four distinct forms: eggs, larvae, pupae and adults. The eggs hatch into larvae, which develop into pupae, which mature into adults and female adults lay eggs under suitable conditions. In this study, each stage in the gonotrophic life cycle of mosquitoes is modeled as compartments governed by a set of differential equations. The progression rates from one compartment to another and the mortality rate in each compartment may vary with temperature. Because only female mosquitoes bite, the daily female adult population computed using this model are used as an input for the Zika transmission model, which is a combination of two other compartmental models, an SEI (Susceptible-Exposed-Infectious) model for the mosquitoes and an SEIR (Susceptible-Exposed-Infectious-Recovered/Removed) model for the humans, such that the infectious mosquitoes infect the susceptible humans and vice versa. The interaction between these three compartmental models and the associated parameters are shown in Figure 1. The differential equations governing these models are discretized on a per day basis and an agent based model (both vectors and humans are agents) is created for the disease transmission.