Studies in disease dynamics [HBNI Th244]

Abstract

Infectious disease are transmitted by pathogens such as bacteria, viruses and parasites. This thesis describes modeling work on two different diseases: COVID-19 and Malaria. The first wave of COVID-19 reached India at the end of January 2020, with the peak in the number of cases occurring in September of that year. The first wave of COVID-19 in India was modelled using a detailed compartmental model for this disease, called INDSCI-SIM. We estimated the undercounting of deaths in the state of Karnataka. For Karnataka as a state and its districts, our estimate for this undercounting range from 2 to 5 across the district. We estimated an undercounting factor of about 2.2 as a whole for the state of Karnataka. We went on to estimate the fraction of those infected by the end of the pandemic, finding that this varied in the range 20% to 70% across Karnataka’s districts. INDSCI-SIM was then used provide a weekly prediction of daily cases and drug require- ments for the state of Andhra Pradesh and its thirteen districts across the period March 2021 to August 1, 2021. This assisted the Government of Andhra Pradesh during the devastating second wave due to the Delta variant. A final project involving COVID-19 was to understand the effect of hybrid immunity i.e. immunity induced by natural infection and vaccination together, in deciding whether schools should have been reopened earlier in the state of Andhra Pradesh. We also studied the overall impact of the Omicron wave. Our model for these problems was a model incorporating vaccinations, that was both age stratified and contact structured. Weighted contact matrices for different location such as home, school, work and others (transport, public places) were used to implement intervention and school reopening. We simulated school reopening scenarios till December 15, 2021, considered as the onset of Omicron wave in India. To estimate the transmission probability of the Omicron wave we calibrated our hybrid immunity model against South Africa’s daily reported cases. We found that no significant rise in cases was to be expected as a consequence of school reopening, for the background seroprevalence of 64%, obtained from our simulation, which is close to the value obtained from the ICMR serosurveydata obtained during June-July, 2021. We also showed that the large vaccination coverage in India before the onset of the Omicron wave protected the population against disease during this wave. We studied the life cycle of the malaria parasite inside different vectors as a function of temperature, for four different regions situated at different altitudes ranging from 1800 m to 3200 m. The temperatures recorded from all four regions for the years 2014 -2015 were used to calculate the extrinsic incubation period of the parasite as a function of temperature and diurnal temperature range, using Bayesian parameter inference. The high elevation region at 3200m was found to be unfavourable for malaria transmission. However at elevations of 2800m, a few months of the year are conducive for transmission. The mid elevation (2200m ) exhibits even more months that are feasible for transmission, while avian malaria transmission can happen throughout the year at low elevations (1800m) region. We also studied parasite range expansion using projected climate data for the range of years 2021-2040, showing that the period of the year where the parasite remains viable is expected to expand due to global warming.