Latin Hypercube Sampling And Infectious Diseases

#Latin Hypercube Sampling And Infectious Diseases Serial Interval Was

Cite this article Matsuyama R, Akhmetzhanov AR, Endo A, Lee H, Yamaguchi T, Tsuzuki S, Nishiura H. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. Model outputs were obtained and the parameter and output values were transformed into their ranks.3 London School of Hygiene & Tropical Medicine, University of London, London, UK DOI 10.7717/peerj.4583 Published Accepted Received Academic Editor Andrew Noymer Subject Areas Computational Biology, Mathematical Biology, Epidemiology, Global Health, Infectious Diseases Keywords Corynebacterium diphtheriae, Diphtheria, Epidemiology, Outbreak, Basic reproduction number, Bangladesh, Refugee, Vaccination, Mathematical model, Statistical estimation Copyright В© 2018 Matsuyama et al. Using Latin hypercube sampling, 100 samples from a uniform distribution of the parameter ranges of both models were taken. Latin hypercube sampling with partial rank correlation coefficient index.

A Rohingya refugee camp in CoxвЂ™s Bazar, Bangladesh experienced a large-scale diphtheria epidemic in 2017. Relationships between the basic reproduction numbers of two network-based ordinary differential equation vector-host models, and. Thresholds for disease extinction contribute crucial knowledge on disease control, elimination, and mitigation of infectious diseases. And controlling infectious diseases / Se servir de la modlisation pour lutter contre les maladies infectieuses by Subject LATIN HYPERCUBE SAMPLING.The reproduction number of deterministic models is an essential quantity to predict whether an epidemic will spread or die out.

Sensitivity analysis indicated that R 0 would become smaller with greater variance of the generation time. R 0 was positively correlated with ascertainment rates. ResultsR 0 ranged from 4.7 to 14.8 with the median estimate at 7.2. To account for the uncertainty of initially immune fraction, we employed a Latin Hypercube sampling (LHS) method. Use latin hypercube sampling to explore the robustness of model results to.A renewal process model was devised to estimate the R 0 and ascertainment rate of cases, and loss of susceptible individuals was modeled as one minus the sum of initially immune fraction and the fraction naturally infected during the epidemic. Methods(F ), 1 is estimated to ensure that the observed fraction of infectious.

Due to widespread use of diphtheriaвЂ“tetanusвЂ“pertussis (DTP) vaccine globally, the incidence has steadily declined over time, and thus, diphtheria is commonly perceived as a disease of pre-vaccination era. Additionally, a gray or white patch causes the вЂњcroup,вЂќ blocking the airway and causing a barking cough. Symptomatic patients initially complain sore throat and fever. LHS was particularly useful in the setting of a refugee camp in which the background health status is poorly quantified.Diphtheria, a bacterial disease caused by Corynebacterium diphtheriae, is a vaccine-preventable disease.

Due to vaccination effort and other countermeasures, including contact tracing and hospital admission of cases, the epidemic has been brought under control, with incidence beginning to decline by the end of December 2017 ( World Health Organization (WHO), 2017a).Considering that diphtheria has become a rare disease in industrialized countries, epidemiological information on model parameters that govern the transmission dynamics has become very limited, and thus, it is valuable to assess how transmissible diphtheria would be through the analysis of the recent outbreak data. To interrupt chains of transmission, emergency vaccination has been conducted among children since December 12, 2017, achieving the overall coverage greater than 90% by the end of 2017 ( World Health Organization (WHO), 2018). As of December 26, 2017, the cumulative number of 2,526 cases and 27 deaths were reported ( World Health Organization (WHO), 2017a). Of these, a Rohingya refugee camp in Bangladesh, which is temporarily located in CoxвЂ™s Bazar, experienced a large-scale diphtheria epidemic.

Given that the epidemic occurred among refugees, we explicitly account for uncertainties associated with unknown background information, including the fraction of previously immune individuals and ascertainment rate of cases. Subsequently, a few additional modeling studies of diphtheria took place ( Kolibo & Romaniuk, 2001 Sornbundit, Triampo & Modchang, 2017 Torrea, Ortega & Torera, 2017), but none of these offered an empirical estimate of R 0.Here we analyze the epidemiological dataset of diphtheria in Rohingya refugee camp, 2017, aiming to estimate R 0 in this particular epidemic setting. To date, an explicit epidemiological estimate of R 0 for diphtheria has been reported only by Anderson & May (1982): using a static modeling approach to age-dependent incidence data with an assumption of the endemic equilibrium, R 0 was estimated as 6.6 in Pennsylvania, 1910s and 6.4 in Virginia and New York from 1934 to 1947.

The definition was improved on and after December 12 ( World Health Organization (WHO), 2017c), enforcing to count mainly probable cases, while not restricting the reporting of suspected cases. Prior to December 11, 2017, cases satisfied only the condition of suspected cases. Diphtheriae by multiplex assay, (ii) probable cases: cases with upper respiratory tract illness with laryngitis or nasopharyngitis or tonsillitis AND sore throat or difficulty swallowing and an adherent membrane (pseudomembrane) OR gross cervical lymphadenopathy, and (iii) suspected cases: any case with a clinical suspicion of diphtheria, including cases that are unclassified due to missing values ( World Health Organization (WHO), 2017b). Cases consist of (i) confirmed cases: cases reported as positive for C. As of Decem(the latest date of observation), a total of 2,526 cases have been reported. Figure 1 shows the latest available epidemic curve.

Latin Hypercube Sampling And Infectious Diseases Serial Interval Was

G П„ represents the distribution of the serial interval. Let i t be the number of new cases on day t. As one of peak time-lags in the observed time interval was observed on day 8, we assumed that the mean serial interval was eight days, and we imposed an assumption that the coefficient of variation (CV) of the serial interval distribution was 50%, and later varied it from 25% to 75% as part of the sensitivity analysis.To capture the epidemiological phenomena of reproduction, it has been shown that the renewal process can capture the serial stochastic dependence structure ( Nishiura, 2010). Following Klinkenberg & Nishiura (2011), the first peak corresponds to an independent infection in the community and the second peak reflects within-household transmission. According to a classical study by Stocks (1930) in the United Kingdom (UK), the time interval from first to second diphtheria cases in the household revealed a bimodal shape.

Let c t be the reported number of cases on calendar day t. It should be noted that the incidence i t includes both symptomatic and asymptomatic cases. The renewal process of this type is not original to the present study and has been applied to other settings including real-time epidemic modeling studies ( Asai & Nishiura, 2017 Dinh et al., 2016 Ejima & Nishiura, 2018 Endo & Nishiura, 2015 Nishiura et al., 2010, 2016 Tsuzuki et al., 2017).

Namely, we set О± t = a 1 for time by December 11 and a 2 on December 12 and later. Because the case definition was improved from Decemonward, the ascertainment rate likely varied around that time.