ABSTRACT
BACKGROUND: Malaria transmission is perennial in the Assam-Arunachal Pradesh interstate border areas in the Sonitpur district of Assam, India. A yearlong study was carried out on the incidence of symptomatic and asymptomatic malaria and the role of asymptomatic malaria carriers in persistent transmission of the disease. The relationships between malaria incidence and weather parameters were also investigated. METHODS: Active and mass blood surveys were conducted on a monthly basis in Bengenajuli, Sapairaumari Pathar, and Nigam villages near the Assam-Arunachal Pradesh border. Epidemiological indices were estimated for malaria-positive cases. Multiple linear regression between monthly malaria incidence and monthly average temperature, and relative humidity along with monthly total rainfall was carried out. The known malaria vectors collected in CDC light traps were identified and recorded. RESULTS: Slide positivity rate (SPR) and Plasmodium falciparum percent (Pf%) for symptomatic malaria were 26.1 and 79.8, respectively. Prevalence of malaria vectors was observed throughout the year with varying density. Anopheles philippinensis/nivipes and A. annularis were predominant among the seven known vector species recorded currently. Asymptomatic parasitemia was detected throughout the year with SPR ranging from 4.8 to 5.3. Monthly rainfall with 1-month lag had the highest correlation (r=0.92) with SPR. The relationship between SPR and weather factors was established as SPR=-114.22+0.58 Tmin+1.38 RH+0.03 RF (R2=0.89; p=0.00). CONCLUSION: Low and relatively constant levels of asymptomatic parasitemia was present in the study area. High malaria vector density and presence of asymptomatic malaria parasite carriers were responsible for persistent malaria transmission in the region. This study concludes that passive detection and prompt treatment of asymptomatic carriers is essential for preventing persistent disease transmission. Rainfall along with some other weather variables may be used for predicting the malaria epidemics in the region. The predictive information could be useful to target resources more effectively.
