Modeling Influenza Seasonality in the Tropics and Subtropics

AbstractClimate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future.

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Modeling influenza seasonality in the tropics and subtropics

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009050 ◽

2021 ◽

Vol 17 (6) ◽

pp. e1009050

Author(s):

Haokun Yuan ◽

Sarah C. Kramer ◽

Eric H. Y. Lau ◽

Benjamin J. Cowling ◽

Wan Yang

Keyword(s):

Hong Kong ◽

Mechanistic Model ◽

Seasonal Pattern ◽

Influenza Viruses ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Influenza Surveillance ◽

Influenza Epidemic ◽

Influenza Transmission ◽

The Impact

Climate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future.

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Dynamic interactions of influenza viruses in Hong Kong during 1998-2018

10.1101/19008987 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wan Yang ◽

Eric H. Y. Lau ◽

Benjamin J. Cowling

Keyword(s):

Hong Kong ◽

Influenza A ◽

Vaccine Development ◽

Immune Escape ◽

Influenza Viruses ◽

Influenza Surveillance ◽

Influenza Epidemic ◽

Inference System ◽

Population Immunity ◽

Cross Immunity

AbstractInfluenza epidemics cause substantial morbidity and mortality every year worldwide. Currently, two influenza A subtypes, A(H1N1) and A(H3N2), and type B viruses co-circulate in humans and infection with one type/subtype could provide cross-protection against the others. However, it remains unclear how such ecologic competition via cross-immunity and antigenic mutations that allow immune escape impact influenza epidemic dynamics at the population level. Here we develop a comprehensive model-inference system and apply it to study the evolutionary and epidemiological dynamics of the three influenza types/subtypes in Hong Kong, a city of global public health significance for influenza epidemic and pandemic control. Utilizing long-term influenza surveillance data since 1998, we are able to estimate the strength of cross-immunity between each virus-pairs, the timing and frequency of punctuated changes in population immunity in response to antigenic mutations in influenza viruses, and key epidemiological parameters over the last 20 years including the 2009 pandemic. We find evidence of cross-immunity in all types/subtypes, with strongest cross-immunity from A(H1N1) against A(H3N2). Our results also suggest that A(H3N2) may undergo antigenic mutations in both summers and winters and thus monitoring the virus in both seasons may be important for vaccine development. Overall, our study reveals intricate epidemiological interactions and underscores the importance of simultaneous monitoring of population immunity, incidence rates, and viral genetic and antigenic changes.

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Increased urbanization reduced the effectiveness of school closures on seasonal influenza epidemics in China

Infectious Diseases of Poverty ◽

10.1186/s40249-021-00911-7 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Hao Lei ◽

Hangjin Jiang ◽

Nan Zhang ◽

Xiaoli Duan ◽

Tao Chen ◽

...

Keyword(s):

Seasonal Influenza ◽

Southern China ◽

Social Contact ◽

School Closure ◽

Surveillance Data ◽

Reproductive Number ◽

Influenza Surveillance ◽

Sirs Model ◽

Influenza Transmission ◽

The Impact

Abstract Background School closure is a common mitigation strategy during severe influenza epidemics and pandemics. However, the effectiveness of this strategy remains controversial. In this study, we aimed to explore the effectiveness of school closure on seasonal influenza epidemics in provincial-level administrative divisions (PLADs) with varying urbanization rates in China. Methods This study analyzed influenza surveillance data between 2010 and 2019 provided by the Chinese National Influenza Center. Taking into consideration the climate, this study included a region with 3 adjacent PLADs in Northern China and another region with 4 adjacent PLADs in Southern China. The effect of school closure on influenza transmission was evaluated by the reduction of the effective reproductive number of seasonal influenza during school winter breaks compared with that before school winter breaks. An age-structured Susceptible-Infected-Recovered-Susceptible (SIRS) model was built to model influenza transmission in different levels of urbanization. Parameters were determined using the surveillance data via robust Bayesian method. Results Between 2010 and 2019, in the less urbanized provinces: Hebei, Zhejiang, Jiangsu and Anhui, during school winter breaks, the effective reproductive number of seasonal influenza epidemics reduced 14.6% [95% confidential interval (CI): 6.2–22.9%], 9.6% (95% CI: 2.5–16.6%), 7.3% (95% CI: 0.1–14.4%) and 8.2% (95% CI: 1.1–15.3%) respectively. However, in the highly urbanized cities: Beijing, Tianjin and Shanghai, it reduced only 5.2% (95% CI: -0.7–11.2%), 4.1% (95% CI: -0.9–9.1%) and 3.9% (95% CI: -1.6–9.4%) respectively. In China, urbanization is associated with decreased proportion of children and increased social contact. According to the SIRS model, both factors could reduce the impact of school closure on seasonal influenza epidemics, and the proportion of children in the population is thought to be the dominant influencing factor. Conclusions Effectiveness of school closure on the epidemics varies with the age structure in the population and social contact patterns. School closure should be recommended in the low urbanized regions in China in the influenza seasons. Graphical abstract

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Whole Genome Sequencing of A(H3N2) Influenza Viruses Reveals Variants Associated with Severity during the 2016–2017 Season

Viruses ◽

10.3390/v11020108 ◽

2019 ◽

Vol 11 (2) ◽

pp. 108 ◽

Cited By ~ 10

Author(s):

Bruno Simon ◽

Maxime Pichon ◽

Martine Valette ◽

Gwendolyne Burfin ◽

Mathilde Richard ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Influenza Viruses ◽

Influenza Surveillance ◽

Whole Genome ◽

University Hospitals ◽

Surveillance Network ◽

Genetic Traits ◽

H3n2 Influenza ◽

The Impact

Influenza viruses cause a remarkable disease burden and significant morbidity and mortality worldwide, and these impacts vary between seasons. To understand the mechanisms associated with these differences, a comprehensive approach is needed to characterize the impact of influenza genomic traits on the burden of disease. During 2016–2017, a year with severe A(H3N2), we sequenced 176 A(H3N2) influenza genomes using next generation sequencing (NGS) for routine surveillance of circulating influenza viruses collected via the French national influenza community-based surveillance network or from patients hospitalized in the intensive care units of the University Hospitals of Lyon, France. Taking into account confounding factors, sequencing and clinical data were used to identify genomic variants and quasispecies associated with influenza severity or vaccine failure. Several amino acid substitutions significantly associated with clinical traits were found, including NA V263I and NS1 K196E which were associated with severity and co-occurred only in viruses from the 3c.2a1 clade. Additionally, we observed that intra-host diversity as a whole and on a specific set of gene segments increased with severity. These results support the use of whole genome sequencing as a tool for the identification of genetic traits associated with severe influenza in the context of influenza surveillance.

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SE2IR Invest Market Rumor Spreading Model Considering Hesitating Mechanism

Journal of Systems Science and Information ◽

10.21078/jssi-2019-054-16 ◽

2019 ◽

Vol 7 (1) ◽

pp. 54-69 ◽

Cited By ~ 3

Author(s):

Hongxing Yao ◽

Xiangyang Gao

Keyword(s):

Mean Field ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Field Equations ◽

Rumor Spreading ◽

Immunization Strategy ◽

Scale Free ◽

Spreading Model ◽

Mean Field Equations ◽

The Impact

Abstract According to the actual situation of investor network, a SE2IR rumor spreading model with hesitating mechanism is proposed, and the corresponding mean-field equations is obtained on scale-free network. In this paper, we first combine the theory of spreading dynamics and find out the basic reproductive number R0. And then analyzes the stability of the rumor-free equilibrium and the final rumor size. Finally, we discuss random immune strategies and target immune strategies for the rumor spreading, respectively. Through numerical simulation, we can draw the following conclusions: Reducing the fuzziness and attractiveness of invest market rumor can effectively reduce the impact of rumor. And the target immunization strategy is more effective than the random immunization strategy for the communicators in the invest investor network.

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Real-time estimation of the influenza-associated excess mortality in Hong Kong

Epidemiology and Infection ◽

10.1017/s0950268819001067 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 1

Author(s):

Jessica Y. Wong ◽

Edward Goldstein ◽

Vicky J. Fang ◽

Benjamin J. Cowling ◽

Peng Wu

Keyword(s):

Hong Kong ◽

Real Time ◽

Excess Mortality ◽

Time Estimation ◽

Surveillance Data ◽

Control Measures ◽

Influenza Surveillance ◽

Mortality Data ◽

Real Time Estimation ◽

The Impact

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

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HEPATITIS C VIRUS AND INTRAVENOUS DRUG MISUSE: A MODELING APPROACH

International Journal of Biomathematics ◽

10.1142/s1793524514500065 ◽

2014 ◽

Vol 07 (01) ◽

pp. 1450006 ◽

Cited By ~ 2

Author(s):

STEADY MUSHAYABASA ◽

CLAVER P. BHUNU

Keyword(s):

Hepatitis C Virus ◽

Hepatitis C ◽

Steady States ◽

Transmission Dynamics ◽

Intravenous Drug ◽

Basic Reproductive Number ◽

Drug Misuse ◽

Dynamical Analysis ◽

Reproductive Number ◽

The Impact

Hepatitis C virus (HCV) is a blood-borne infection that can lead to progressive liver failure, cirrhosis, hepatocellular carcinoma and death. A deterministic mathematical model for assessing the impact of daily intravenous drug misuse on the transmission dynamics of HCV is presented and analyzed. A threshold quantity known as the reproductive number has been computed. Stability of the steady states has been investigated. The dynamical analysis reveals that the model has globally asymptotically stable steady states. The impact of daily intravenous drug misuse on the transmission dynamics of HCV has been discussed through the basic reproductive number and numerical simulations.

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A Simulation on Potential Secondary Spread of Novel Coronavirus in an Exported Country Using a Stochastic Epidemic SEIR Model

Journal of Clinical Medicine ◽

10.3390/jcm9040944 ◽

2020 ◽

Vol 9 (4) ◽

pp. 944 ◽

Cited By ~ 15

Author(s):

Kentaro Iwata ◽

Chisato Miyakoshi

Keyword(s):

Healthcare Professionals ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Asian Countries ◽

Hospital Visit ◽

Seir Model ◽

Stochastic Epidemic ◽

Novel Coronavirus ◽

The Impact ◽

Multiple Scenarios

Ongoing outbreak of pneumonia caused by novel coronavirus (2019-nCoV) began in December 2019 in Wuhan, China, and the number of new patients continues to increase. Even though it began to spread to many other parts of the world, such as other Asian countries, the Americas, Europe, and the Middle East, the impact of secondary outbreaks caused by exported cases outside China remains unclear. We conducted simulations to estimate the impact of potential secondary outbreaks in a community outside China. Simulations using stochastic SEIR model were conducted, assuming one patient was imported to a community. Among 45 possible scenarios we prepared, the worst scenario resulted in the total number of persons recovered or removed to be 997 (95% CrI 990–1000) at day 100 and a maximum number of symptomatic infectious patients per day of 335 (95% CrI 232–478). Calculated mean basic reproductive number (R0) was 6.5 (Interquartile range, IQR 5.6–7.2). However, better case scenarios with different parameters led to no secondary cases. Altering parameters, especially time to hospital visit. could change the impact of a secondary outbreak. With these multiple scenarios with different parameters, healthcare professionals might be able to better prepare for this viral infection.

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Real-time estimation of the hospitalization fatality risk of influenza A(H1N1)pdm09 in Hong Kong

Epidemiology and Infection ◽

10.1017/s0950268815003179 ◽

2015 ◽

Vol 144 (8) ◽

pp. 1579-1583

Author(s):

J. Y. WONG ◽

P. WU ◽

E. H. Y. LAU ◽

T. K. TSANG ◽

V. J. FANG ◽

...

Keyword(s):

Hong Kong ◽

Real Time ◽

Influenza A ◽

Early Stage ◽

Influenza Pandemic ◽

Influenza Viruses ◽

Right Censoring ◽

Fatality Risk ◽

Influenza A H1n1 ◽

The Impact

SUMMARYDuring the early stage of an epidemic, timely and reliable estimation of the severity of infections are important for predicting the impact that the influenza viruses will have in the population. We obtained age-specific deaths and hospitalizations for patients with laboratory-confirmed H1N1pdm09 infections from June 2009 to December 2009 in Hong Kong. We retrospectively obtained the real-time estimates of the hospitalization fatality risk (HFR), using crude estimation or allowing for right-censoring for final status in some patients. Models accounting for right-censoring performed better than models without adjustments. The risk of deaths in hospitalized patients with confirmed H1N1pdm09 increased with age. Reliable estimates of the HFR could be obtained before the peak of the first wave of H1N1pdm09 in young and middle-aged adults but after the peak in the elderly. In the next influenza pandemic, timely estimation of the HFR will contribute to risk assessment and disease control.

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The heterogeneous severity of COVID-19 in African countries: A modeling approach

10.21203/rs.3.rs-426664/v1 ◽

2021 ◽

Author(s):

Salihu Sabiu Musa ◽

Xueying Wang ◽

Shi Zhao ◽

Shudong Li ◽

Nafiu Hussaini ◽

...

Keyword(s):

Case Fatality ◽

Well Being ◽

Mitigation Strategies ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Time Varying ◽

African Countries ◽

Awareness Campaigns ◽

Considerable Impact ◽

The Impact

Abstract Background: The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated through fitting epidemic models to the reported COVID-19 deaths.Method: We downloaded data for the twelve most affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number (R0(t)) and infection attack rate (IAR). We developed a simple epidemic model and fitted the model to reported COVID-19 deaths in twelve African countries using iterated filtering and allowing a flexible transmission rate.Results: We observed high heterogeneity in the case-fatality rate across countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were affected most strongly, exhibiting a relatively higher(R0(t)) and infection attack rate.Conclusion: To effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).

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