Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India

Abstract

The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.

The Great Influenza Pandemic of 1918–20: An interpretative survey in the time of COVID-19

“…When estimated at the mid-point of Barro et al's and Davis's estimates (reported in the table), British India accounted for 45 per cent of total global pandemic deaths 13 . There were notable spatial differences in deaths in the subcontinent, with western, northwestern, and central regions recording much higher mortality rates (Reyes et al 2018;Chandra and Kassens-Noor 2014;Mills 1986;Gill 1928). The relative importance of differences in diurnal temperature, comorbidity, food scarcity, and other related factors that determined people's susceptibility to the virus remains an unresolved issue.…”

The 1918 Influenza Pandemic: Looking Back, Looking Forward

“…Active research topics in the field of archeo-epidemiology include the search for predictors of influenza mortality, such as socioeconomic indicators or geography, and the drivers of influenza spatial diffusion. In 2 articles in the present issue, the authors concentrated on the spatial diffusion of influenza, focusing on British India and Portugal, 2 countries that have been poorly studied in the context of the 1918 pandemic ( 10 , 11 ). Both studies revealed a highly heterogeneous spread of the pandemic and geographic variation in pandemic mortality impact, albeit at different spatial scales.…”

“…Although Portugal as a whole was severely hit by the pandemic compared with other European countries, some provinces nearly fully escaped ( 10 ). Analysis of district-level mortality records in India revealed a northeastward wave of infection from September to November 1918 that was associated with climate and population density ( 11 ). Diffusion was driven by long-distance jumps via the railroad network, superimposed on local diffusion between neighboring provinces.…”

“…Five of the 12 articles touch on the origins of the 1918 pandemic virus, addressing the role of swine as mixing vessels in this and other pandemic events ( 5 ), the age-specific mortality patterns of the pandemic ( 6 – 8 ), and prior population immunity ( 9 ). Others include reports on geographic and social heterogeneities in the pandemic experience in which the authors describe the spatial diffusion of the pandemic in India and Portugal ( 10 , 11 ), the socioeconomic predictors of high mortality risk in Sweden and globally ( 12 , 13 ), and the consequences of the pandemic on US natality rates ( 14 , 15 ). Finally, 2 commentaries address preparedness for future influenza pandemics ( 16 , 17 ).…”

Utilization of hot spot analysis in the detection of spatial determinants and clusters of the Spanish flu mortality

“…The detection of spatial manifestation of the demographic phenomena, such as mortality, enables a better understanding of the spread of the Spanish flu, as well as other infectious diseases throughout history. It can also be applied in modern spatial demography and spatial epidemiology for designing more efficient public health interventions for future outbreaks with similar characteristics (Chowell, Bettencourt, Johnson, Alonso, & Viboud, 2007) and for developing public health countermeasures and implement effective mitigation plans (Reyes et al, 2018).…”