Predicted pattern of Zika virus infection distribution with reference to rainfall in Thailand

Somsri W iwanitkit， Viroj W iwanitkit

1Wiwanitkit House， Bangkhae， Bangkok， Thailand2Hainan Medical University， Haikou 571199， Hainan， China



Predicted pattern of Zika virus infection distribution with reference to rainfall in Thailand

Somsri W iwanitkit1*， Viroj W iwanitkit2

1Wiwanitkit House， Bangkhae， Bangkok， Thailand
2Hainan Medical University， Haikou 571199， Hainan， China

AR T ICLE IN FO

Article history:

Received 15 April 2016

Received in revised form 16 May 2016 Accepted 15 June 2016

Available online 20 July 2016

Keywords:Zika virus Infection Rainfall

ABSTRACT

Zika virus infection is the present global medical problem. The disease appears in several countries around the world. The relationship between rainfall and occurrence of Zika virus infection was previously mentioned. Here， the authors use the mathematical modeling technique to reappraise on the previous data on immunoreactivity rate of Zika virus， dengue virus and Ckikungunya virus in Thailand and the reported interrelationship between arboviral infections and rainfall in Thailand for constructing of the predicted pattern of Zika virus distribution in Thailand. This data can be a useful tool for further disease surveillance in this area.

Email: somsriw iwan@hotmail.com

1. Introduction

Zika virus infection is the present global medical problem［1-3］. The disease can cause dengue-like illness and the infection in the pregnant can result in congenital anomaly［1-3］. A t present， this disease appears in several countries around the world with the significant number of infections in tropical countries in South America［1-3］. As a mosquito borne infection， the effect of climate on this infection is mentioned. The relationship between rainfall and occurrence of Zika virus infection was previously noted［4］.

Here， the authors use the mathematical modeling technique to reappraise on the previous data on immunoreactivity rate of Zika virus， dengue virus and Ckikungunya virus in Thailand and the reported interrelationship between arboviral infections and rainfall in Thailand for constructing of the predicted pattern of Zika virus distribution in Thailand.

2. Materials and methods

2.1. Previous data in Thailand

The main previous data used for the present study refer to the previous publications. The previous publication I Asian Pac J Trop Med by Wikan et al is used for referencing to immunoreactivity rate of Zika virus infection， dengue virus and Ckikungunya virus in Thailand［5］. The other two publications［6，7］ on reported interrelationship betw een arboviral infections and rainfall in Thailand are also used as basic data for modeling in the present study.

2.2. Mathematical modeling

The mathematical modeling technique is used for generating of the predicted pattern of Zika virus infection distribution with reference to rainfall in Thailand. First， the predicted rates of Zika virus infection relating to the other two common arboviral infections in Thailand， dengue virus and Ckikungunya virus， were calculated based on the data in the report by Wikan et al［5］. The derived rates were used for further adjustment to the previously reported equations on interrelationship between arboviralinfection rate， prevalence， and rainfall in the previous referencing report［6，7］. Summarization of the derived equation into the final equation for predicting the prevalence of Zika virus infection pattern relating to rainfall in Thailand was done.

3. Results

3.1. Primary basic data

According to the report by Wikan et al［5］， it can be calculated that The rate of concomitant Zika virus immunoreactivity in cases with dengue virus immunoreactivity and Ckikungunya virus immunoreactivity is equal to 13/17 （76.47%） and 10/13 （76.92%），respectively. The previous report relationship between rainfall and dengue prevalence by Wiwanitkit［6］ is as the follow ing ‘the least square equation plot prevalence （y） versus rainfall （x） is Y = 3.0X + 4.6’［6］ and the previous report relationship between rainfall and Ckikungunya infection prevalence by Wiwanitkitand Wiwanitkit［7］is as the follow ing ‘the derived least square equation plot prevalence （Y） versus rainfall （X） was Y= 0.8X+ 0.6’［7］.

3.2. Modeling

Based on the derived relative immunoreactivity rate of Zika virus，the adjusted equation based on dengue and Ckikungunya virus infection situations can be ‘Y = 2.29X + 3.52’（equation A） and ‘Y = 0.64X + 0.48’ （equation B）， respectively； giving Y = prevalence of Zika virus infection （/100 000） and X = rainfall （inch）. Further manipulation to summarization of equation A and equation B can yield the final equation ‘Y = 1.47X + 2’. /using this derived relationship， the geographic information system （GIS） map showing the relationship can be constructed as shown in Figure 1.

Figu re 1. Predicted prevalence （case/100 000 population） of Zika virus infection in Thailand.

4. Discussion

As a mosquito borne infection， it is no doubt that climate factor becomes an important determ inant for occurrence of Zika virus infection. The interrelationship between rainfall and occuence of infection was firsty proposed by Althouse et al［4］. Here， the authors use the mathematical technique to construct the new model for predicting the possible pattern of Zika virus prevalence in Thailand in case that there w ill be the future outbreak. The relationship between rainfall and other sim ilar common arboviral disease is used as the template for the model. Based on the present study， the derived model shows that the rate of infection， in case of existed outbreak， w ill be sim ilar high to the rate of dengue. This means the disease w ill becomes another major problematic arboviral disease in the country. In fact， the disease m ight already exist and spread in Thailand but it is under diagnosed because the infected cases can be asymptomatic or mild symptomatic［1，3］. Of interest，the data in his short communication is the first world report on the predicted interrelationship between Zika virus infection prevalence and rainfall. This data can be a useful tool for further disease surveillance in this area.

Conflict of interest statement

We declare that we have no conflict of interest.

References

［1］ Joob B， Wiwanitkit V. Zika virus infection and dengue: a new problem in diagnosis in a dengue-endem ic area. Ann Trop Med Public Health 2015；8（4）: 145-146.

［2］ Wiwanitkit S， Wiwanitkit V. Acute viral hemorrhage disease: a summary on new viruses. J Acute Dis 2015； 4（4）: 277-279.

［3］ W iwanitkit S， W iwanitkit V. A febrile， asym ptomatic and nonthrombocytopenic Zika virus infection: don’t m iss it！ Asian Pac J Trop Med 2016 （in press）. doi:10.1016/j.apjtm.2016.03.036.

［4］ Althouse BM， Hanley KA， Diallo M， Sall AA， Ba Y， Faye O， et al. Impact of climate and mosquito vector abundance on sylvatic arbovirus circulation dynam ics in Senegal. Am J Trop Med Hyg 2015； 92（1）: 88-97.

［5］ W ikan N， Suputtamongkol Y， Yoksan S， Sm ith DR， Auewarakul P. Immunological evidence of Zika virus transm ission in Thailand. Asian Pac J Trop Med 2016； 9（2）:141-144.

［6］ Wiwanitkit V. An observation on correlation between rainfall and the prevalence of clinical cases of dengue in Thailand. J Vector Borne Dis 2006； 43（2）: 73-76.

［7］ Wiwanitkit S， Wiwanitkit V. Ckikungunya virus infection and relationship to rainfall， the relationship study from southern Thailand. J Arthropod Borne Dis 2013； 7（2）: 185-187.

doi:Document heading 10.1016/j.apjtm.2016.05.014

*Corresponding author:Somsri Wiwanitkit， W iwanitkit House， Bangkhae， Bangkok Thailand.