# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## 5 Discussion  
The scale of the 2015-2016 Zika epidemic in Americas has raised public health concerns worldwide. Understanding the dynamics of the disease requires the investigation of the major drivers of a vector-borne disease outbreak: the mosquito population and human travel patterns. A dynamic model that simulates the transmission of Zika in 23 islands of French Polynesia incorporating a temperature dependent mosquito ecology and the local transit network was presented in the study. If base case values for the mosquito population (initial vector population is three times the human population) and transit network (100% load on the network ) are used, then 86% of the total population in the affected islands were infected by the end of the outbreak, which lasted for about 160 days. Additionally, the model can be used to test the effectiveness of intervention strategies like mosquito and bite control, and travel bans. Mosquito control measures like biocontrol and mosquito bite prevention help in containing the outbreak by reducing the probability of transmission. Implementing travel bans or advisories even after the first reported case was shown to be ineffective on this network because of the combination of high connectivity of the nodes and under reporting. Even though the disease reached all the islands eventually, reduction in the load introduces a delay in the start of the outbreak which can be used to prepare for the outbreak. The basic reproduction number (\(R_{0}\)) for the entire population was calculated to be 2.135 (CI 95%: 2.07-2.20) using the Next Generation Matrix. These results are in line with the calculated reproduction numbers for the French Polynesia outbreak by other studies [6]. Although \(R_{0}\) is used to predict the scale of future outbreaks, it is sensitive to demographic and geographical circumstances. Therefore, \(R_{0}\) calculated for a disease in one region should not be used to predict the disease outcomes for a different geographic and demographic location [39]. This is emphasized further by the wide range of \(R_{0}\) values published for Zika in the literature which vary significantly from one region to another [6; 40; 41; 42]. The process described to calculate \(R_{0}\) using the next-generation matrix can be applied to other heterogeneous epidemiological systems like Lyme disease, plague, malaria, dengue, etc. by changing the differential equations and the parameters associated with them. The availability of demographic, climatic, and transit data was critical in developing the model discussed in the paper. The proposed model can be used to study and control the outbreak of other  
Figure 10: The variation in the size of the outbreak with change in the threshold cases (\(\tau\)) when the bite rate is reduced to 50% (distribution of epidemic size). vector-borne diseases in other regions globally if the aforementioned data are available. This highlights the importance of data collection, particularly on the size of the mosquito population and high risk areas, so that contact rates can be altered accordingly and vulnerable populations/areas can be identified. For example, the number of mosquito breeding sites or control measures may vary across islands, and therefore using a uniform mosquito to human ratio is not the most accurate. Collecting the data for these variables can improve the accuracy of the results and facilitate region specific predictions and planning. The results from this study show that public-health planners need to be proactive with mitigation strategies when cases are being reported in neighboring regions. This is particularly critical for neighboring regions with high volumes of commuter traffic. Activating any strategy only after a case has been detected in the affected jurisdiction is often too late to make a meaningful impact on the severity of an outbreak. Thus, if a case is detected in a region, enacting preventative measures i.e., mosquito net distribution and fumigation, in neighboring regions will aid in reducing the size of the outbreak significantly.