Conjunction of factors triggering waves of seasonal influenza

Chattopadhyay, Ishanu; Kıcıman, Emre; Elliott, Joshua; Shaman, Jeffrey; Rzhetsky, Andrey

doi:10.7554/elife.30756

Cited by 42 publications

(43 citation statements)

References 75 publications

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“…To estimate the incidence of each betacoronavirus, we used an incidence proxy calculated by multiplying the weekly percentage of positive tests for each coronavirus by the weekly population-weighted proportion of physician visits due to influenza-like illness (ILI) (13).…”

Section: Methodsmentioning

confidence: 99%

“…In terms of transmission, the ability of SARS-CoV-2 to cause widespread infection is more reflective of HCoV-OC43 and HCoV-HKU1 than of its more clinically severe relatives. HCoV-OC43 and HCoV-HKU1 cause annual wintertime outbreaks of respiratory illness in temperate regions (9,10), suggesting that wintertime climate and host behaviors may facilitate transmission, as is true for influenza (11)(12)(13). Immunity to HCoV-OC43 and HCoV-HKU1 appears to wane appreciably within one year (14), while SARS infection can induce longer-lasting immunity (15).…”

Section: Abstract (Limit 125 Words)mentioning

confidence: 99%

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Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period

Kissler

Tedijanto

Goldstein

et al. 2020

Preprint

218

160

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limit 125 words).There is an urgent need to project how transmission of the novel betacoronavirus SARS-CoV-2 will unfold in coming years. These dynamics will depend on seasonality, the duration of immunity, and the strength of cross-immunity to/from the other human coronaviruses. Using data from the United States, we measured how these factors affect transmission of human betacoronaviruses HCoV-OC43 and HCoV-HKU1. We then built a mathematical model to simulate transmission of SARS-CoV-2 through the year 2025. We project that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after an initial pandemic wave. We summarize the full range of plausible transmission scenarios and identify key data still needed to distinguish between them, most importantly longitudinal serological studies to determine the duration of immunity to SARS-CoV-2.Main text. The ongoing SARS-CoV-2 epidemic has caused nearly 80,000 detected cases of COVID-19 illness and claimed over 2,500 lives as of 24 Feb 2020 (1). With sustained transmission reported in China, Japan, Iran, Italy, and South Korea (1), the outbreak is on the verge of becoming a pandemic. The required intensity, duration, and urgency of public health responses will depend on how the initial pandemic wave unfolds and on the subsequent transmission dynamics of SARS-CoV-2. One possibility is that SARS-CoV-2 will follow its closest genetic relative, SARS-CoV, and be eradicated by intensive public health measures after causing a brief but intense epidemic (2). Increasingly, public health authorities consider this scenario unlikely (3). Alternatively, the transmission of SARS-CoV-2 could resemble that of pandemic influenza by circulating seasonally after causing an initial global wave of infection (4).Such a scenario could reflect the previous emergence of known human coronaviruses from zoonotic origins e.g. human coronavirus (HCoV) OC43 (5). This paper identifies viral, environmental, and immunologic factors which in combination will determine which scenarios in fact play out, and identifies key data needed to distinguish between them.The transmission dynamics of the SARS-CoV-2 epidemic will depend on factors including the degree of seasonal variation in transmission strength, the duration of immunity, and the degree of cross-immunity between SARS-CoV-2 and other coronaviruses. SARS-CoV-2 belongs to the betacoronavirus genus, which includes the SARS coronavirus, MERS coronavirus, and two other human coronaviruses, HCoV-OC43 and HCoV-HKU1. The SARS and MERS coronaviruses cause severe illness with case fatality rates of 9 and 36% respectively, but the transmission of both has remained limited (6). HCoV-OC43 and HCoV-HKU1 infections may be asymptomatic or associated with mild to moderate upper respiratory tract illness; these HCoVs are considered the second most common cause of the common cold (6). While investigations into the spectrum of illness caused by SARS-CoV-2 are ongoing, recent evidence indicates the majority of cases experience mild illness with more limited occu...

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Section: Methodsmentioning

confidence: 99%

Section: Abstract (Limit 125 Words)mentioning

confidence: 99%

Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period

Kissler

Tedijanto

Goldstein

et al. 2020

Preprint

218

160

View full text Add to dashboard Cite

show abstract

“…In addition, this locally structured populations may exhibit clique behavior. Moreover, in many cases one can find a spatial epidemic wave pattern depending on the virus type and location [24,21,25]. This assumption, which to some extent holds true even today when people move longer distances over shorter times, may smooth-out the clique behavior.…”

Section: The Modelmentioning

confidence: 99%

Multi-season analysis reveals the spatial structure of disease spread

Seroussi

Levy

Yom‐Tov

2020

Physica A: Statistical Mechanics and its Applications

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Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies. Here, we develop a novel tensor-driven multi-compartment version of the classic Susceptible-Infected-Recovered (SIR) model and apply it to Internet data to reveal information about the complex spatial structure of disease spread. The model is used to analyze state-level Google search data from the US pertaining to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV). We fit the data with correlations of R 2 = 0.70, and 0.52 for RSV and WNV, respectively. Although no prior assumptions on spatial structure are made, human movement patterns in the US explain 27-30% of the estimated inter-state transmission rates. The transmission rates within states are correlated with known demographic indicators, such as population density and average age. Finally, we show that the patterns of disease load for subsequent seasons can be predicted using the model parameters estimated for previous seasons and as few as 7 weeks of data from the current season. Our results are applicable to other countries and similar viruses, allowing the identification of disease spread parameters and prediction of disease load for seasonal viruses earlier in season.

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“…The model covariates included COVID-19 risk perception, influenza risk perception, humidity (shown to be an important driver of influenza dynamics [15,16]), and strain dynamics. To measure COVID-19 risk perception, we collected the weekly mean number of Google Trends searches of "coronavirus", "COVID", and "COVID-19" at the national and state level [17,18].…”

Section: Regression Modelmentioning

confidence: 99%

Double trouble? When a pandemic and seasonal virus collide

Zipfel

Bansal

2020

Preprint

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The 2019-2020 influenza sentinel surveillance data exhibits unexpected trends. Typical influenza seasons have a small herald wave, followed by a decrease due to school closure during holidays, and then a main post-holiday peak that is significantly larger than the pre-holiday wave. During the 2019-2020 influenza season, influenza-like illness data in the United States appears to have a markedly lower main epidemic peak compared to what would be expected based on the pre-holiday peak. We hypothesize that the 2019-2020 influenza season does have a lower than expected burden and that this deflation is due to a behavioral or ecological interaction with COVID-19. We apply an intervention analysis to assess if this influenza season deviates from expectations, then we compare multiple hypothesized drivers of the decrease in influenza in a spatiotemporal regression model. Lastly, we develop a mechanistic metapopulation model, incorporating transmission reduction that scales with COVID-19 risk perception. We find that the 2019-2020 ILI season is smaller and decreases earlier than expected based on prior influenza seasons, and that the increase in COVID-19 risk perception is associated with this decrease. Additionally, we find that a 5% average reduction in transmission is su cient to reproduce the observed flu dynamics. We propose that precautionary behaviors driven by COVID-19 risk perception or increased isolation driven by undetected COVID-19 spread dampened the influenza season. We suggest that when surveillance for a novel pathogen is limited, surveillance streams of co-circulating infections may provide a signal.

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Conjunction of factors triggering waves of seasonal influenza

Cited by 42 publications

References 75 publications

Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period

Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period

Multi-season analysis reveals the spatial structure of disease spread

Double trouble? When a pandemic and seasonal virus collide

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