Projecting the seasonality of endemic COVID-19

Abstract Importance Successive waves of infection by SARS-CoV-2 have left little doubt that COVID-19 will transition to an endemic disease, yet the future seasonality of COVID-19 remains one of its most consequential unknowns. Foreknowledge of spatiotemporal surges would have immediate and long-term consequences for medical and public health decision-making.Objective To estimate the impending endemic seasonality of COVID-19 in temperate population centers via a phylogenetic ancestral and descendent states approach that leverages long-term data on the incidence of circulating coronaviruses.Design We performed a comparative evolutionary analysis on literature-based monthly verified cases of HCoV-NL63, HCoV-229E, HCoV-HKU1, and HCoV-OC43 infection within populations across the Northern Hemisphere. Ancestral and descendent states analyses on human-infecting coronaviruses provided projections of the impending seasonality of endemic COVID-19.Setting Quantitative projections of the endemic seasonality of COVID-19 were based on human endemic coronavirus infection incidence data from New York City (USA); Denver (USA); Tampere (Finland); Trøndelag (Norway); Gothenburg (Sweden); Stockholm (Sweden); Amsterdam (Netherlands); Beijing (China); South Korea (Nationwide); Yamagata (Japan); Hong Kong; Nakon Si Thammarat (Thailand); Guangzhou (China); and Sarlahi (Nepal).Main Outcome(s) and Measure(s) The primary projection was the monthly relative frequency of SARS-CoV-2 infections in each geographic locale. Four secondary outcomes consisted of empirical monthly relative frequencies of the endemic human-infecting coronaviruses HCoV-NL63, -229E, -HKU1, and -OC43.Results We project asynchronous surges of SARS-CoV-2 across locales in the Northern Hemisphere. In New York City, SARS-CoV-2 incidence is projected in late fall and winter months (Nov.–Jan.), In Tampere, Finland; Yamagata, Japan; and Sarlahi, Nepal incidence peaks in February. Gothenburg and Stockholm in Sweden reach peak incidence between November and February. Guangzhou, China; and South Korea. In Denver, incidence peaks in early Spring (Mar.). In Amsterdam, incidence rises in late fall (Dec.), and declines in late spring (Apr.). In Hong Kong, the projected apex of infection is in late fall (Nov.–Dec.), yet variation in incidence is muted across other seasons. Seasonal projections for Nakhon Si Thammarat, Thailand and for Beijing, China are muted compared to other locations.Conclusions and Relevance This knowledge of likely spatiotemporal surges of COVID-19 is fundamental to medical preparedness and expansions of public health interventions that anticipate the impending endemicity of this disease and mitigate COVID-19 transmission. These results provide crucial guidance for adaptive public health responses to this disease, and are vital to the long-term mitigation of COVID-19 transmission.Key Points Question Under endemic conditions, what are the projected spatiotemporal seasonal surges of COVID-19?Findings We applied a phylogenetic ancestral and descendent states approach, leveraging long-term data on the incidence of circulating coronaviruses. We found that seasonal surges are expected in or near the winter months; dependent on the specific population center, infections are forecasted to surge in the late fall, winter, or early spring.Meaning Globally, endemic COVID-19 surges should be expected to occur asynchronously, often coincident with local expected surges of other human-infecting respiratory viruses..

Medienart:	Preprint

Erscheinungsjahr:	2023
Erschienen:	2023

Enthalten in:	bioRxiv.org - (2023) vom: 16. Nov. Zur Gesamtaufnahme - year:2023

Sprache:	Englisch

Beteiligte Personen:	Townsend, Jeffrey P. [VerfasserIn] Lamb, April D. [VerfasserIn] Hassler, Hayley B. [VerfasserIn] Sah, Pratha [VerfasserIn] Nishio, Aia Alvarez [VerfasserIn] Nguyen, Cameron [VerfasserIn] Tew, Alexandra D. [VerfasserIn] Galvani, Alison P. [VerfasserIn] Dornburg, Alex [VerfasserIn]

Links:	Volltext [kostenfrei]

Themen:	570 Biology

doi:	10.1101/2022.01.26.22269905

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	XBI035098457