Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold

Postnikov, Eugene B.

doi:10.1140/epjb/e2015-50845-7

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…The number of newly diagnosed pulmonary tuberculosis patients and the adequate contact rate show seasonal fluctuations, as is seen in many respiratory infections such as influenza and measles [22–25]. The number of confirmed patients in February was slightly lower, probably due to a decrease in the number of hospital visits during the one-week Spring Festival holiday.…”

Section: Discussionmentioning

confidence: 99%

Epidemiological characteristics of pulmonary tuberculosis in mainland China from 2004 to 2015: a model-based analysis

et al. 2019

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Background We used data released by the government to analyze the epidemiological distribution of pulmonary tuberculosis in mainland China from 2004 to 2015, in order to provide a deeper understanding of trends in the epidemiology of pulmonary tuberculosis in China and a theoretical basis to assess the effectiveness of government interventions and develop more targeted prevention and control strategies. Methods A discrete dynamic model was designed based on the epidemiological characteristics of pulmonary tuberculosis and fitted to data published by the government to estimate changes in indicators such as adequate contact rate, prevalence of non-treated pulmonary tuberculosis (abbreviated as prevalence), and infection rate. Finally, we performed sensitivity analyses of the effects of parameters on the population infection rate. Results The epidemiological features of pulmonary tuberculosis in China include a pattern of seasonal fluctuations, with the highest rates of infection in autumn and winter. The adequate contact rate has increased slightly from an average of 0.12/month in 2010 to an average of 0.21/month in 2015. The prevalence in the population has continued to decrease from 3.4% in early 2004 to 1.7% in late 2015. The Mycobacterium tuberculosis ( M. tuberculosis ) infection rate in the population decreased gradually from 42.3% at the beginning of 2004 to 36.7% at the end of 2015. The actual number of new infections gradually decreased from 1,300,000/year in 2010 to 1,100,000/year in 2015. The actual number of new patients each year has been relatively stable since 2010 and remains at approximately 2,600,000/year. Conclusions The population prevalence and the M. tuberculosis infection rate have decreased year by year since 2004, indicating that the tuberculosis epidemic in China has been effectively controlled. However, pulmonary tuberculosis has become increasingly contagious since 2010. China should focus on the prevention and control of pulmonary tuberculosis during autumn and winter. Electronic supplementary material The online version of this article (10.1186/s12889-019-6544-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Epidemiological characteristics of pulmonary tuberculosis in mainland China from 2004 to 2015: a model-based analysis

et al. 2019

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“…wide geographic area, see the respective discussion in [18] ); τ is the characteristic duration of illness.…”

Section: Introductionmentioning

confidence: 99%

Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?

Postnikov

2020

Chaos, Solitons & Fractals

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203

174

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Basing on existence of the mathematically sequential reduction of the three-compartmental (Susceptible-Infected-Recovered/Removed) model to the Verhulst (logistic) equation with the parameters determined by the basic characteristic of epidemic process, this model is tested in application to the recent data on COVID-19 outbreak reported by the European Centre for Disease Prevention and Control. It is shown that such a simple model adequately reproduces the epidemic dynamics not only qualitatively but for a number of countries quantitatively with a high degree of correlation that allows to use it for predictive estimations. In addition, some features of SIR model are discussed in the context, how its parameters and conditions reflect measures attempted for the disease growth prevention that is also clearly indicated by deviations from such model solutions.

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“…Numerous studies try to explain flu-like seasonality with meteorological factors such as sunlight, including UV radiation (Schuit et al, 2020), temperature and humidity (Chong et al, 2020; Shaman et al, 2011). However, Postnikov (2016) concluded that ambient temperature is not a good predictor for influenza seasonality in the Netherlands, and inconsistent correlations also exist for the relationships between COVID-19 and temperature (Toseupu et al, 2020; Xie & Zhu, 2020; Ma et al 2020; Qi et al, 2020). Furthermore, findings about the relationships between humidity and influenza (Soebiyanto et al, 2014), and humidity and COVID-19 (Ahmadi et al, 2020; Ma et al, 2020; Qi et al, 2020) are equally inconsistent.…”

Section: Introductionmentioning

confidence: 99%

Pollen Explains Flu-Like and COVID-19 Seasonality

Hoogeveen

Gorp

Hoogeveen

2020

Preprint

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Current models for flu-like epidemics insufficiently explain multi-cycle seasonality. Meteorological factors alone, including associated behavior, do not predict seasonality, given substantial climate differences between countries that are subject to flu-like epidemics or COVID-19. Pollen is documented to be allergenic, plays a role in immuno-activation, and seems to create a bio-aerosol lowering the reproduction number of flu-like viruses. Therefore, we hypothesize that pollen may explain the seasonality of flu-like epidemics including COVID-19 in conjunction with meteorological variables. We tested the Pollen-Flu Seasonality Theory for 2016-2020 flu-like seasons, including COVID-19, in The Netherlands with its 17 million inhabitants. We combined changes in flu-like incidence per 100K/Dutch citizens (code: ILI) with weekly pollen concentrations and meteorological data. Finally, a discrete, predictive model is tested using pollen and meteorological threshold values displaying inhibitory effects on flu-like incidence. We found a highly significant inverse correlation of r(224)= -0.38 (p < 0.00001) between pollen and changes in flu-like incidence corrected for incubation period. The correlation was stronger after taking into account incubation time, which satisfies the temporality criteria. We found that our predictive model has the highest inverse correlation with changes in flu-like incidence of r(222) = -0.48 (p < 0.001) when average thresholds of 610 total pollen grains/m3, 120 allergenic pollen grains/m3, and a solar radiation of 510 J/cm2 are passed. The passing of at least the pollen thresholds, preludes the beginning and end of flu-like seasons. Solar radiation is a co-inhibitor of flu-like incidence, temperature makes no difference, and higher relative humidity associates even with flu-like incidence increases. We conclude that pollen is a predictor of the inverse seasonality of flu-like epidemics including COVID-19, and that solar radiation is a co-inhibitor. The observed seasonality of COVID-19 during Spring, suggests that COVID-19 may revive in The Netherlands after week 33, preceded by the relative absence of pollen, and follows pollen-flu seasonality patterns.

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Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold

Cited by 8 publications

References 24 publications

Epidemiological characteristics of pulmonary tuberculosis in mainland China from 2004 to 2015: a model-based analysis

Epidemiological characteristics of pulmonary tuberculosis in mainland China from 2004 to 2015: a model-based analysis

Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?

Pollen Explains Flu-Like and COVID-19 Seasonality

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