A Framework to Monitor Changes in Transmission and Epidemiology of Emerging Pathogens: Lessons From Nipah Virus

Abstract

It is of uttermost importance that the global health community develops the surveillance capability to effectively monitor emerging zoonotic pathogens that constitute a major and evolving threat for human health. In this study, we propose a comprehensive framework to measure changes in (1) spillover risk, (2) interhuman transmission, and (3) morbidity/mortality associated with infections based on 6 epidemiological key indicators derived from routine surveillance. We demonstrate the indicators’ value for the retrospective or real-time assessment of changes in transmission and epidemiological characteristics using data collected through a long-standing, systematic, hospital-based surveillance system for Nipah virus in Bangladesh. We show that although interhuman transmission and morbidity/mortality indicators were stable, the number and geographic extent of spillovers varied significantly over time. This combination of systematic surveillance and active tracking of transmission and epidemiological indicators should be applied to other high-risk emerging pathogens to prevent public health emergencies.

The ecology of Nipah virus in Bangladesh: a nexus of land use change and opportunistic feeding behavior in bats

“…Based on spillover events from 2001-2018, we confirmed previous analyses [38,40,44] showing that Nipah virus spillovers are spatially clustered within districts in the central and northwest regions of Bangladesh ( Figure 1A). Outbreak years vary in the intensity of spillover and winter is the primary season when spillovers occur throughout the country ( Figure 1B,C), although there are occasional events in early spring in central Bangladesh.…”

“…Cases prior to 2007 were detected through community investigations following reports of clusters of encephalitis. Cases from 2007 onward reflect those identified through systematic surveillance for Nipah virus infection at three tertiary care hospitals combined with investigations of all cases detected to look for clusters, as well as any reports of possible outbreaks through media or other information sources [38]. Independent spillover events were defined as index cases of Nipah virus infection within a given outbreak year.…”

“…First, there is substantial year-to-year variation in the number of Nipah virus spillover events in Bangladesh [38] that may be explained by ecological factors influencing bat behavior and viral shedding. Cortes et al [40] showed that differences in winter temperature can explain variation in Nipah virus spillovers, but this analysis only covered the period 2007-2013 and missed the decrease in spillovers observed after 2015 [38]. Second, we lack comprehensive information on the population biology, roosting and feeding behavior, and movement ecology of P. medius in Bangladesh.…”

Assessing the feasibility of Nipah vaccine efficacy trials based on previous outbreaks in Bangladesh

“…In the cluster randomized mass vaccination trial design, individuals were randomized at the level of small administrative areas to receive vaccination. Two geographic scales for enrollment were considered: (i) the two districts (Faridpur and Rajbari districts, with a total population of ~3 million individuals), or (ii) the five districts (additionally including Naogaon, Rangpur, and Gopalganj districts, with a total population of ~6.8 million individuals) with highest reported detection rates of spillovers from the zoonotic reservoir into humans [7]. The vaccine efficacy was estimated by comparing the total number of cases detected by study arm.…”

“…For each NiV case we drew the number of secondary cases (i.e. cases infected through interhuman transmission) from a negative binomial distribution with reproduction number R=0.20 and overdispersion parameter k=0.06 as observed for cases reported during 2007-2014 (a time period of systematic NiV surveillance) [6,7]. For each secondary NiV case, we drew the time from disease onset in the infector to infection from a discretized gamma distribution with mean 4 days and standard deviation (SD) 2 days and the time from infection to symptom onset in the secondary case from a gamma distribution with mean 10 days and SD 2 days [6].…”