Modelling of the influenza A(H1N1)v outbreak in Mexico City, April-May 2009, with control sanitary measures

Cruz-Pacheco, Gustavo; Duran, Laurie; Esteva, Luis; Minzoni, Antonmaria A.; López-Cervantes, Malaquı́as; Panayotaros, Panayotis; Ortega, A Ahued; Ruíz, I Villaseñor

doi:10.2807/ese.14.26.19254-en

Cited by 44 publications

(16 citation statements)

References 6 publications

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“…One study in Hong Kong showed that school closure during the 2009 H1N1 pandemic reduced the effective reproductive number by 11.8% [ 32 ]. In Mexico and New Zealand, there also have been reported decreases in R t during school closure [ 33 , 34 ]. Contrastingly, in the 2009 pandemic in USA there was no clear effect of school closure detected on transmission [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…Various estimates of the reproductive number have been made for the initial phase of the epidemic in different regions of the world [15,[17][18][19][20][21][22][23][24][25][26]. These estimates range from 0.5 to 3.4 [17,27].…”

Section: Estimating the Effective Reproductive Numbermentioning

confidence: 99%

Modelling the initial phase of an epidemic using incidence and infection network data: 2009 H1N1 pandemic in Israel as a case study

Katriel

Yaari

Huppert

et al. 2011

J. R. Soc. Interface.

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This paper presents new computational and modelling tools for studying the dynamics of an epidemic in its initial stages that use both available incidence time series and data describing the population's infection network structure. The work is motivated by data collected at the beginning of the H1N1 pandemic outbreak in Israel in the summer of 2009. We formulated a new discrete-time stochastic epidemic SIR (susceptible-infected-recovered) model that explicitly takes into account the disease's specific generation-time distribution and the intrinsic demographic stochasticity inherent to the infection process. Moreover, in contrast with many other modelling approaches, the model allows direct analytical derivation of estimates for the effective reproductive number (R e ) and of their credible intervals, by maximum likelihood and Bayesian methods. The basic model can be extended to include age -class structure, and a maximum likelihood methodology allows us to estimate the model's next-generation matrix by combining two types of data: (i) the incidence series of each age group, and (ii) infection network data that provide partial information of 'whoinfected-who'. Unlike other approaches for estimating the next-generation matrix, the method developed here does not require making a priori assumptions about the structure of the next-generation matrix. We show, using a simulation study, that even a relatively small amount of information about the infection network greatly improves the accuracy of estimation of the next-generation matrix. The method is applied in practice to estimate the next-generation matrix from the Israeli H1N1 pandemic data. The tools developed here should be of practical importance for future investigations of epidemics during their initial stages. However, they require the availability of data which represent a random sample of the real epidemic process. We discuss the conditions under which reporting rates may or may not influence our estimated quantities and the effects of bias.

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“…Societal reactions can be grouped into different classes. First, there are changes imposed by authorities through the closure of schools, churches, public offices, and bans on public gatherings [25][26][27][28]. Second, individuals self-initiate behavioral changes due to the concern induced by the disease [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Towards a Characterization of Behavior-Disease Models

et al. 2011

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The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.

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Modelling of the influenza A(H1N1)v outbreak in Mexico City, April-May 2009, with control sanitary measures

Cited by 44 publications

References 6 publications

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

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

Modelling the initial phase of an epidemic using incidence and infection network data: 2009 H1N1 pandemic in Israel as a case study

Towards a Characterization of Behavior-Disease Models

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