The most common routes of transmission in these outbreaks are person-to-person transmission. However, the recently published literature suggested that the COVID-19 was more contagious than previous coronavirus outbreaks, SARS-CoV and MERS-CoV (Bai, Nie, & Wen, 2020; Li et al., 2020a; de Wit, van Doremalen, Falzarano, & Munster, 2016). The basic reproduction number (R 0), as the average number of secondary infections produced by an infected case in an entirely susceptible population (Ferguson,
BackgroundMalaria re-introduction is a challenge in elimination settings. To prevent re-introduction, receptivity, vulnerability, and health system capacity of foci should be monitored using appropriate tools. This study aimed to design an applicable model to monitor predicting factors of re-introduction of malaria in highly prone areas.MethodsThis exploratory, descriptive study was conducted in a pre-elimination setting with a high-risk of malaria transmission re-introduction. By using nominal group technique and literature review, a list of predicting indicators for malaria re-introduction and outbreak was defined. Accordingly, a checklist was developed and completed in the field for foci affected by re-introduction and for cleared-up foci as a control group, for a period of 12 weeks before re-introduction and for the same period in the previous year. Using field data and analytic hierarchical process (AHP), each variable and its sub-categories were weighted, and by calculating geometric means for each sub-category, score of corresponding cells of interaction matrices, lower and upper threshold of different risks strata, including low and mild risk of re-introduction and moderate and high risk of malaria outbreaks, were determined. The developed predictive model was calibrated through resampling with different sets of explanatory variables using R software. Sensitivity and specificity of the model were calculated based on new samples.ResultsTwenty explanatory predictive variables of malaria re-introduction were identified and a predictive model was developed. Unpermitted immigrants from endemic neighbouring countries were determined as a pivotal factor (AHP score: 0.181). Moreover, quality of population movement (0.114), following malaria transmission season (0.088), average daily minimum temperature in the previous 8 weeks (0.062), an outdoor resting shelter for vectors (0.045), and rainfall (0.042) were determined. Positive and negative predictive values of the model were 81.8 and 100 %, respectively.ConclusionsThis study introduced a new, simple, yet reliable model to forecast malaria re-introduction and outbreaks eight weeks in advance in pre-elimination and elimination settings. The model incorporates comprehensive deterministic factors that can easily be measured in the field, thereby facilitating preventive measures.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1192-y) contains supplementary material, which is available to authorized users.
This retrospective study aimed to address whether or to what extent spatial and non-spatial factors with a focus on a healthcare delivery system would influence successful tuberculosis (TB) treatment outcomes in Urmia, Iran. In this cross-sectional study, data of 452 new TB cases were extracted from Urmia TB Management Center during a 5-year period. Using the Geographical Information System (GIS), health centers and study subjects' locations were geocoded on digital maps. To identify the statistically significant geographical clusters, Average Nearest Neighbor (ANN) index was used. Logistic regression analysis was employed to determine the association of spatial and non-spatial variables on the occurrence of adverse treatment outcomes. The spatial clusters of TB cases were concentrated in older, impoverished and outskirts areas. Although there was a tendency toward higher odds of adverse treatment outcomes among urban TB cases, this finding after adjusting for distance from a given TB healthcare center did not reach statistically significant. This article highlights effects of spatial and non-spatial determinants on the TB adverse treatment outcomes, particularly in what way the policies of healthcare services are made. Accordingly, non-spatial determinants in terms of low socio-economic factors need more attention by public health policy makers, and then more focus should be placed on the health delivery system, in particular men's health.
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