Investigation of Key Interventions for Shigellosis Outbreak Control in China

Chen, Tianmu; Leung, R. C. K.; Zhou, Zhongjun; Liu, Ruchun; Zhang, Xixing; Zhang, Lijie

doi:10.1371/journal.pone.0095006

Cited by 44 publications

(75 citation statements)

References 22 publications

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“…In the model, people and viruses have different dimensions. Based on our previous research [15], we therefore used the following sets to perform the normalization:…”

Section: The Simplified Reservoir-people Transmission Network Modelmentioning

confidence: 99%

A mathematical model for simulating the phase-based transmissibility of a novel coronavirus

et al. 2020

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Background: As reported by the World Health Organization, a novel coronavirus (2019-nCoV) was identified as the causative virus of Wuhan pneumonia of unknown etiology by Chinese authorities on 7 January, 2020. The virus was named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by International Committee on Taxonomy of Viruses on 11 February, 2020. This study aimed to develop a mathematical model for calculating the transmissibility of the virus. Methods: In this study, we developed a Bats-Hosts-Reservoir-People transmission network model for simulating the potential transmission from the infection source (probably be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from Huanan Seafood Wholesale Market (reservoir) to people, we simplified the model as Reservoir-People (RP) transmission network model. The next generation matrix approach was adopted to calculate the basic reproduction number (R 0 ) from the RP model to assess the transmissibility of the SARS-CoV-2. Results: The value of R 0 was estimated of 2.30 from reservoir to person and 3.58 from person to person which means that the expected number of secondary infections that result from introducing a single infected individual into an otherwise susceptible population was 3.58. Conclusions: Our model showed that the transmissibility of SARS-CoV-2 was higher than the Middle East respiratory syndrome in the Middle East countries, similar to severe acute respiratory syndrome, but lower than MERS in the Republic of Korea.

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“…In the model, people and viruses have different dimensions. Based on our previous research [15], we therefore used the following sets to perform the normalization:…”

Section: The Simplified Reservoir-people Transmission Network Modelmentioning

confidence: 99%

A mathematical model for simulating the phase-based transmissibility of a novel coronavirus

et al. 2020

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“…Although statistical model, like Bayesian model, was employed to explore the epidemiological characteristics [18], there is few research to simulate and forecast the population-based transmission [19]. To explore the transmissibility, we have performed a study to estimate the relative transmissibility of shigellosis among male and female individuals in Hubei Province, China [20].…”

Section: Introductionmentioning

confidence: 99%

Epidemiological characteristics and transmissibility of shigellosis in Hubei Province, China, 2005 – 2017

Chen

Rui

et al. 2020

BMC Infect Dis

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Background: Shigellosis is one of the main diarrhea diseases in developing countries. However, the transmissibility of shigellosis remains unclear. Methods: We used the dataset of shigellosis cases reported between January 2005 and December 2017, from Hubei Province, China. A mathematical model was developed based on the natural history and the transmission mechanism of the disease. By fitting the data using the model, transmission relative rate from person to person (b) and from reservoir to person (b w ), and the effective reproduction number (R eff ) were estimated. To simulate the contribution of b and b w during the transmission, we performed a "knock-out" simulation in four scenarios:Results: A total of 130,770 shigellosis cases were reported in Hubei province, among which 13 cases were dead. The median annual incidence was 19.96 per 100,000 persons (range: 5.99 per 100,000 persons -29.47 per 100,000 persons) with a decreased trend (trend χ 2 = 25,470.27, P < 0.001). The mean values of b and b w were 0.0898 (95% confidence interval [CI]: 0.0851-0.0946) and 1.1264 × 10 − 9 (95% CI: 4.1123 × 10 − 10 -1.8416 × 10 − 9 ), respectively. The "knock-out" simulation showed that the number of cases simulated by scenario A was almost the same as scenario B, and scenario C was almost the same as scenario D. The mean value of R eff of shigellosis was 1.19 (95% CI: 1.13-1.25) and decreased slightly with a Linear model until it decreased to an epidemic threshold of 0.99 (95% CI: 0.65-1.34) in 2029. Conclusions:The incidence of shigellosis is still in high level. The transmissibility of the disease is low in Hubei Province. The transmission would be interrupted in the year of 2029.

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“…The details of model-fitting methods run in Berkeley Madonna, such as the Runge–Kutta method of order 4 and root-mean-square deviation, were the same as the those in references [1, 2, 12]. …”

Section: Methodsmentioning

confidence: 99%

The transmissibility estimation of influenza with early stage data of small-scale outbreaks in Changsha, China, 2005–2013

Chen

et al. 2016

Epidemiol. Infect.

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SUMMARYHundreds of small-scale influenza outbreaks in schools are reported in mainland China every year, leading to a heavy disease burden which seriously impacts the operation of affected schools. Knowing the transmissibility of each outbreak in the early stage has become a major concern for public health policy-makers and primary healthcare providers. In this study, we collected all the small-scale outbreaks in Changsha (a large city in south central China with ~7·04 million population) from January 2005 to December 2013. Four simple and popularly used models were employed to calculate the reproduction number (R) of these outbreaks. Given that the duration of a generation interval Tc = 2·7 and the standard deviation (s.d.) σ = 1·1, the mean R estimated by an epidemic model, normal distribution and delta distribution were 2·51 (s.d. = 0·73), 4·11 (s.d. = 2·20) and 5·88 (s.d. = 5·00), respectively. When Tc = 2·9 and σ = 1·4, the mean R estimated by the three models were 2·62 (s.d. = 0·78), 4·72 (s.d. = 2·82) and 6·86 (s.d. = 6·34), respectively. The mean R estimated by gamma distribution was 4·32 (s.d. = 2·47). We found that the values of R in small-scale outbreaks in schools were higher than in large-scale outbreaks in a neighbourhood, city or province. Normal distribution, delta distribution, and gamma distribution models seem to more easily overestimate the R of influenza outbreaks compared to the epidemic model.

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Investigation of Key Interventions for Shigellosis Outbreak Control in China

Cited by 44 publications

References 22 publications

A mathematical model for simulating the phase-based transmissibility of a novel coronavirus

A mathematical model for simulating the phase-based transmissibility of a novel coronavirus

Epidemiological characteristics and transmissibility of shigellosis in Hubei Province, China, 2005 – 2017

The transmissibility estimation of influenza with early stage data of small-scale outbreaks in Changsha, China, 2005–2013

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