Abstract
Zika (ZIKV) is emerging and re-emerging arboviral diseases. These viruses are transmitted to humans through the bites of Aedes mosquitoes. Recently, ZIKV infection has been described as an emerging disease in Thailand and many countries, especially in tropical and sub-tropical areas. The specific drugs and vaccines against these infections are unavailable; therefore, effective disease control relies on vector control measures only. To understand the transmission cycle of these viruses, mosquito vectors and human. This study is designed 1) to determine seasonal ZIKV infection rates in mosquitoes for prediction the outbreak of ZIKV infection, and 2) to develop and validate a Zika-RT-LAMP (SYBR). The present studies detected very few positive samples of ZIKV and chikungunya viruses in mosquitoes by hn-RT-PCR, collected from 4 provinces which were proposed. However, we detected the chikungunya virus in mosquito samples collected from the outbreak areas in Bangkok, and we successfully performed whole genome sequencing with these samples. The Zika-RT-LAMP (SYBR) assay showed high sensitivity and specificity. The detection limit of the ZIKV-RT-LAMP assay was 10-6 ffu/ml and detected the Asian lineage of ZIKV RNA without cross-reactivity with other arthropod-borne viruses. In addition, the researchers used dengue fever data as a prototype to test the developed model by applying ontology to enhance the classification efficiency of decision tree algorithms. These findings have provided information regarding pathogen-pathogen interaction in host cell. The data from this study could be used for future development of more effective prevention, control strategies, and predict outbreaks of ZIKV in Thailand.
