Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host-pathogen-protein interactions, combined with a better understanding of a host's immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination. (Less)

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Background: In spite of existing vaccination programs in many countries, outbreaks of pertussis are still reported. In Iran, the suspected and confirmed cases of pertussis are reported annually. Due to the lack of similar studies, the purpose of the current study was to determine the Spatio-temporal distribution of Pertussis using Geographic Information System (GIS) to identify high-risk areas in Iran during 2012-2018. Methods: In the current cross-sectional study, registered data in the department of vaccine-preventable diseases in the Iranian ministry of health were used. To assess the temporal trend, the Cochran-Armitage test was used. To show the spatial distribution and to identify hotspot areas, Choropleth map and Getis-Ord Gi statistics were used. All analyses performed by Arc.map10.5, Stata 15 and Excel 2010. Results: The incidence of suspect pertussis cases had an increasing trend but did not have a linear trend (p=0.06). Most of the cases happened in under 1 year infants (62.66%). The incidence of reported cases in northern areas was higher than in the Southern areas. The Zanjan had the most reported cases during the understudied period with a median of 7.63 reported cases per 100,000. The clustering of infection and hotspots were identified in northern areas of Iran including Qazvin, Qom, Markazi, and Hamadan. Conclusion: Our results showed that the cumulative incidence of reported cases is increasing. The northern provinces had the highest incidence of Pertussis. Therefore, the causes of this spatio-temporal pattern of pertussis should be determined. Also, supervision on vaccination programs in high-risk areas is recommended.

http://mjiri.iums.ac.ir/article-1-6074-en.pdf

Spatio-temporal analysis of Pertussis using geographic information system among Iranian population during 2012-2018

Background Pertussis is a toxin-mediated respiratory illness caused by Bordetella pertussis that can result in severe complications and death, particularly in infants. Between 2008 and 2011, children less than 3 months old accounted for 83% of the pertussis deaths in the United States. Understanding the geographic disparities in the distribution of pertussis risk and identifying high risk geographic areas is necessary for guiding resource allocation and public health control strategies. Therefore, this study investigated geographic disparities and temporal changes in pertussis risk in Florida from 2010 to 2018. It also investigated socioeconomic and demographic predictors of the identified disparities. Methods Pertussis data covering the time period 2010-2018 were obtained from Florida HealthCHARTS web interface. Spatial patterns and temporal changes in geographic distribution of pertussis risk were assessed using county-level choropleth maps for the time periods 2010-2012, 2013-2015, 2016-2018 and 2010-2018. Tango's flexible spatial scan statistics were used to identify high-risk spatial clusters which were displayed in maps. Ordinary least squares (OLS) regression was used to identify significant predictors of county-level risk. Residuals of the OLS model were assessed for model assumptions including spatial autocorrelation. Results County-level pertussis risk varied from 0 to 116.31 cases per 100,000 people during the study period. A total of 11 significant (p < 0.05) spatial clusters were identified with risk ratios ranging from 1.5 to 5.8. Geographic distribution remained relatively consistent over time with areas of high risk persisting in the western panhandle, northeastern coast, and along the western coast. Although county level pertussis risks generally increased from 2010-2012 to 2013-2015, risk tended to be lower during the 2016-2018 time period. Significant predictors of county-level pertussis risk were rurality, percentage of females, and median income. Counties with high pertussis risk tended to be rural (p = 0.021), those with high median incomes (p = 0.039), and those with high percentages of females (p < 0.001). Conclusion There is evidence that geographic disparities exist and have persisted over time in Florida. This study highlights the application and importance of Geographic Information Systems (GIS) technology and spatial statistical/epidemiological tools in identifying areas of highest disease risk so as to guide resource allocation to reduce health disparities and improve health for all.

Geographic disparities and socio-demographic predictors of pertussis risk in Florida.

Objectives This study aims to explore the spatial and spatiotemporal distribution of pertussis in Hunan Province, and provide a scientific basis for targeting preventive measures in areas with a high incidence of pertussis. Design In this retrospective spatial and spatiotemporal (ecological) study, the surveillance and population data of Hunan Province from 2009 to 2019 were analysed. The ArcGIS V.10.3 software was used for spatial autocorrelation analysis and visual display, and SaTScan V.9.6 software was used for statistical analysis of spatiotemporal scan data. Settings Confirmed and suspected pertussis cases with current addresses in Hunan Province and onset dates between 1 January 2009 and 31 December 2019 were included in the study. Participants The study used aggregated data, including 6796 confirmed and suspected pertussis cases. Results The seasonal peak occurred between March and September, and scattered children were at high risk. The global Moran’s I was between 0.107 and 0.341 (p<0.05), which indicated that the incidence of pertussis in Hunan had a positive spatial autocorrelation. The results of local indicators of spatial autocorrelation analysis showed that the hot spots were mainly distributed in the northeast region of Hunan Province. Moreover, both purely space and spatiotemporal scans showed that the central and northeastern parts were the most likely cluster areas with an epidemic period between March and October in 2018 and 2019. Conclusion The distribution of the pertussis epidemic in Hunan Province from 2009 to 2019 shows spatiotemporal clustering. The clustering areas of the pertussis epidemic were concentrated in the central and northeastern parts of Hunan Province between March and October 2018 and 2019. In areas with low pertussis incidence, the strengthening of the monitoring system may reduce under-reporting. In areas with high pertussis incidence where we could study whether the genes of endemic pertussis strains are mutated and differ from vaccine strains.

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Spatiotemporal analysis of pertussis in Hunan Province, China, 2009–2019

Increased numbers of pertussis cases in September 2015 led to the declaration of an outbreak in the Saskatoon Health Region (SHR). SHR (population approximately 350,000) is a geographic area in central Saskatchewan consisting of both urban and rural municipalities. The purpose of this study was to describe the epidemiology and identify possible predictors of the outbreak. Confirmed cases of pertussis in SHR from 2010 to 2015 were extracted from the integrated Public Health Information System (iPHIS) database. Univariate and bivariate analyses and a comparison of the two outbreaks were conducted. Poisson regression modelling was used to estimate incidence rate ratios (IRRs) of factors associated with pertussis infection. Two outbreaks between 2010 and 2015. Factors associated with the 2015 outbreak were residence in rural areas (IRR = 18.67, 95% CI 11.82–29.49; 11.37, 95% CI 6.40–20.21; and 6.31, 95% CI 3.43–11.62) for Humboldt, Watrous, and Rosthern areas, respectively, compared to the City of Saskatoon, and among children 11–14 years of age (IRR = 3.11, 95% CI 1.67–5.79) compared to children under 5 years of age. Unvaccinated persons had increased risk (IRR = 1.60, 95% CI 1.07–2.38). Multiple interventions, including enhanced contact tracing, supplemental immunization clinics, and cocooning, were employed in the 2015 outbreak. Pertussis is a cyclical disease with outbreaks occurring every 3 to 5 years. Teenagers have increased risk of disease compared to younger children, likely due to waning immunity. Rural residents had a higher incidence of disease, possibly due to clusters of conscientious objectors. Control efforts require recognition of waning immunity and unvaccinated susceptibles.

Judith Wright

Papers

Predictors of pertussis outbreak in urban and rural municipalities of Saskatchewan, Canada.