scholarly journals Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1312
Author(s):  
Eliseos J. Mucaki ◽  
Ben C. Shirley ◽  
Peter K. Rogan
Keyword(s):  
Public Health ◽  
Real Time ◽  
Disease Transmission ◽  
Time Integration ◽  
Community Level ◽  
Moran’S I ◽  
Contact Tracing ◽  
Term Care ◽  
Moran's I ◽  
Community Transmission

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources.  Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

scholarly journals Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

2021 ◽  
Author(s):  
Eliseos J. Mucaki ◽  
Ben C. Shirley ◽  
Peter K. Rogan
Keyword(s):  
Public Health ◽  
Real Time ◽  
Disease Transmission ◽  
Time Integration ◽  
Community Level ◽  
Moran’S I ◽  
Contact Tracing ◽  
Term Care ◽  
Moran's I ◽  
Community Transmission

AbstractIntroductionThis study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario, Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals.MethodsCOVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area [FSA], and postal codes [PC] in municipal regions covering Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources.Results/DiscussionThis study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

scholarly journals Estimating COVID Risk During a Period of Pandemic Decline

2021 ◽  
Vol 9 ◽  
Author(s):  
Timothy J. J. Inglis ◽  
Benjamin McFadden ◽  
Anthony Macali
Keyword(s):  
Public Health ◽  
Real Time ◽  
Health Resources ◽  
Risk Estimate ◽  
Contact Tracing ◽  
Business Continuity ◽  
Risk Monitoring ◽  
Open Source Data ◽  
Travel Restrictions ◽  
Community Transmission

Background: Many parts of the world that succeeded in suppressing epidemic coronavirus spread in 2020 have been caught out by recent changes in the transmission dynamics of SARS-CoV-2. Australia's early success in suppressing COVID-19 resulted in lengthy periods without community transmission. However, a slow vaccine rollout leaves this geographically isolated population vulnerable to leakage of new variants from quarantine, which requires internal travel restrictions, disruptive lockdowns, contact tracing and testing surges.Methods: To assist long term sustainment of limited public health resources, we sought a method of continuous, real-time COVID-19 risk monitoring that could be used to alert non-specialists to the level of epidemic risk on a sub-national scale. After an exploratory data assessment, we selected four COVID-19 metrics used by public health in their periodic threat assessments, applied a business continuity matrix and derived a numeric indicator; the COVID-19 Risk Estimate (CRE), to generate a daily spot CRE, a 3 day net rise and a seven day rolling average. We used open source data updated daily from all Australian states and territories to monitor the CRE for over a year.Results: Upper and lower CRE thresholds were established for the CRE seven day rolling average, corresponding to risk of sustained and potential outbreak propagation, respectively. These CRE thresholds were used in a real-time map of Australian COVID-19 risk estimate distribution by state and territory.Conclusions: The CRE toolkit we developed complements other COVID-19 risk management techniques and provides an early indication of emerging threats to business continuity.

scholarly journals Geographical variations and associated factors of defaulting from immunization among children aged 12 to 23 months in Ethiopia: using spatial and multilevel analysis of 2016 Ethiopian Demographic and Health Survey

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Mukemil Awol ◽  
Zewdie Aderaw Alemu ◽  
Nurilign Abebe Moges ◽  
Kemal Jemal
Keyword(s):  
Multilevel Analysis ◽  
Health Survey ◽  
Associated Factors ◽  
Demographic And Health Survey ◽  
Community Level ◽  
Scan Statistics ◽  
Moran’S I ◽  
Moran's I ◽  
Depth Analysis ◽  
Global Moran’S I

Abstract Background In Ethiopia, despite the considerable improvement in immunization coverage, the burden of defaulting from immunization among children is still high with marked variation among regions. However, the geographical variation and contextual factors of defaulting from immunization were poorly understood. Hence, this study aimed to identify the spatial pattern and associated factors of defaulting from immunization. Methods An in-depth analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS 2016) data was used. A total of 1638 children nested in 552 enumeration areas (EAs) were included in the analysis. Global Moran’s I statistic and Bernoulli purely spatial scan statistics were employed to identify geographical patterns and detect spatial clusters of defaulting immunization, respectively. Multilevel logistic regression models were fitted to identify factors associated with defaulting immunization. A p value < 0.05 was used to identify significantly associated factors with defaulting of child immunization. Results A spatial heterogeneity of defaulting from immunization was observed (Global Moran’s I = 0.386379, p value < 0.001), and four significant SaTScan clusters of areas with high defaulting from immunization were detected. The most likely primary SaTScan cluster was seen in the Somali region, and secondary clusters were detected in (Afar, South Nation Nationality of people (SNNP), Oromiya, Amhara, and Gambella) regions. In the final model of the multilevel analysis, individual and community level factors accounted for 56.4% of the variance in the odds of defaulting immunization. Children from mothers who had no formal education (AOR = 4.23; 95% CI: 117, 15.78), and children living in Afar, Oromiya, Somali, SNNP, Gambella, and Harari regions had higher odds of having defaulted immunization from community level. Conclusions A clustered pattern of areas with high default of immunization was observed in Ethiopia. Both the individual and community-level characteristics were statistically significant factors of defaulting immunization. Therefore, the Federal Ethiopian Ministry of Health should prioritize the areas with defaulting of immunization and consider the identified factors for immunization interventions.

scholarly journals Spatial distribution and determinant factors of anemia among adults aged 15–59 in Ethiopia; using mixed-effects ordinal logistic regression model

BMC Nutrition ◽  
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Biruk Shalmeno Tusa ◽  
Sewnet Adem Kebede ◽  
Adisu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background Anemia is a global public health problem, particularly in developing countries. Assessing the geographic distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anemia. Thus, the current study is aimed to assess the spatial distribution and determinant factors of anemia in Ethiopia among adults aged 15–59. Methods A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anemia. Result The spatial distribution of anemia in Ethiopia among adults age 15–59 was found to be clustered (Global Moran’s I = 0.81, p value <  0.0001). In the multivariable mixed-effectordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], highly educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults. Conclusions A significant clustering of anemia among adults aged 15–59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, sex, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed for the identified hotspot areas and risk groups in order to decrease the incidence of anemia.

scholarly journals Hotspots of female genital mutilation/cutting and associated factors among girls in Ethiopia: a spatial and multilevel analysis

2020 ◽  
Author(s):  
Tesfahun Taddege Geremew ◽  
Muluken Azage ◽  
Endalkachew Worku
Keyword(s):  
Female Genital Mutilation ◽  
Associated Factors ◽  
Spatial Clustering ◽  
Household Wealth ◽  
Community Level ◽  
Moran’S I ◽  
Moran's I ◽  
Factors Affecting ◽  
Genital Mutilation ◽  
Female Genital

Abstract Background: Female genital mutilation/cutting (FGM/C) is a harmful traditional practice that violates the human rights of girls and women. It is widely practiced mainly in Africa including Ethiopia. There are a number of studies on the prevalence of FGM/C in Ethiopia. However, little has been devoted to its spatial epidemiology and associated factors. Hence, this study aimed to explore the spatial pattern and factors affecting FGM/C among girls in Ethiopia.Methods: A further analysis of the 2016 Ethiopia Demographic and Health Survey data was conducted, and a total of 6,985 girls nested in 603 enumeration areas were included. Moran's I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Kulldorff’s spatial scan statistics were used to detect spatial clusters of FGM/C. Multilevel logistic regression models were fitted to identify individual and community level factors affecting FGM/C.Results: Spatial clustering of FGM/C was observed (Moran’s I=0.31, p-value < 0.01), and eight significant clusters of FGM/C were detected. The most likely primary cluster was detected in the neighborhood areas of Amhara, Afar, Tigray and Oromia regions (LLR = 279.0, p< 0.01), the secondary cluster in Tigray region (LLR=67.3, p<0.01), and the third cluster in Somali region (LLR=55.5, P<0.01). In About 83% variation in the odds of FGM/C was attributed to both individual and community level factors. At individual level, older maternal age, higher number of living children, maternal circumcision, perceived believes as FGM/C is required by religion, and supporting the continuation of FGM/C practice were factors to increase the odds of FGM/C, whereas, secondary/higher maternal education, better household wealth, and media exposure were factors decreasing the odds of FGM/C. Place of residency, Region and Ethnicity were the community level factors associated with FGM/C.Conclusions: Spatial clustering of FGM/C among girls was observed, and FGM/C hotspots were detected in Afar, Amhara, Tigray, BenishangulGumuz, Oromia, SNNPR and Somali regions including Dire Dawa Town. Both individual and community level factors play a significant role in the practice of FGM/C. Hence, FGM/C hotspots require priority interventions, and it is also better to consider both individual and community level factors.

scholarly journals Spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia; using mixed-effects ordinal logistic regression model

2021 ◽  
Author(s):  
biruk shalmeno tusa ◽  
Sewnet Adem kebede ◽  
Adisu Birhanu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background: Anaemia is a global public health problem particularly in developing countries. Assessing the geographical distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anaemia. Thus, the current study aimed to assess the spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia.Methods: A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of Anaemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of Anaemia.Result: The spatial distribution of anemia among adults age 15-59 was found to be clustered in Ethiopia (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effect ordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], higher educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults.Conclusions: A significant clustering of anemia among adults aged 15-59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, gender, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed in the identified hotspot areas and risk groups to decrease the incidence of anaemia.

scholarly journals Real Time Viral Sub-Strains Discovery in Emerging Infectious Disease Situation – The African Perspective

2021 ◽  
Author(s):  
Moses Effiong Ekpenyong ◽  
Faith-Michael Uzoka ◽  
Mercy Edoho ◽  
Udoinyang G. Inyang ◽  
Ifiok J Udo ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Real Time ◽  
Disease Transmission ◽  
Large Scale ◽  
Expression Patterns ◽  
Deep Understanding ◽  
Contact Tracing ◽  
Reference Sequence ◽  
African Countries ◽  
Significant Difference

Abstract Background: The increased number of accessible genomes has prompted large-scale comparative studies for decerning evolutionary knowledge of infectious diseases, but challenges such as non-availability of close reference sequence(s), incompletely assembled or large number of genomes, preclude real time multiple sequence alignment and sub-strain(s) discovery. This paper introduces a cooperatively inspired open-source framework, for intelligent mining of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genomes. We situate this study within the African context, to drive advancement on state-of-the-art, towards intelligent infectious disease characterization and prediction. The outcome is an enriched Knowledge Base, sufficient to provide deep understanding of the viral sub-strains’ identification problem. We also open investigation by gender, which to the best of our knowledge has been ignored in related research. Data for the study came from the Global Initiative on Sharing All Influenza Data database (https://gisaid.org) and processed for precise discovery of viral sub-strains transmission between and within African countries. To localize the transmission route(s) of each isolate excavated and provide appropriate links to similar isolate strain(s), a cognitive solution was imposed on the genome expression patterns discovered by unsupervised self-organizing map (SOM) component planes visualization. The Freidman-Nemenyi’s test was finally performed to validate our claim. Results: Evidence of inter- and intra-genome diversity was noticed. While some isolates (or genomes) clustered differently, implying different evolutionary source (or high-diversity), others clustered closely together, indicating similar evolutionary source (or less-diversity). SOM component planes analysis revealed multiple sub-strains patterns, strongly suggesting local- or intra-community and country to country transmissions. Cognitive maps of both male and female isolates revealed multiple transmission routes. Freidman’s test results showed highly significant difference (p<0.01) among the various isolate groups. Nemenyi’s test revealed groups that differed in their isolates.Conclusion: The proposed framework offers explanations to SARS-CoV-2 diversity and provides real time identification to disease transmission routes, as well as rapid decision support for facilitating inter- and intra-country contact tracing of infected case(s). Intermediate data produced in this paper are helpful to enrich the genome datasets for intelligent characterization and prediction of COVID-19 and related pandemics, as well as the construction of intelligent device for accurate infectious disease monitoring.

scholarly journals Containing SARS-CoV-2 in Belize

2020 ◽  
Author(s):  
Kyle Habet ◽  
Diomne Habet ◽  
Gliselle Marin
Keyword(s):  
Public Health ◽  
Early Intervention ◽  
Population Density ◽  
Central America ◽  
National Level ◽  
Contact Tracing ◽  
Limited Resources ◽  
Global Pandemic ◽  
Caribbean Country ◽  
Community Transmission

Belize is a small Caribbean country in Central America with limited resources in public health. Amidst a global pandemic, urgent attention was given to mitigating the spread of SARS-CoV-2 (COVID-19) in order to prevent a public health catastrophe. Early intervention on a national level was key to preventing the importation of cases and subsequent community transmission. Limiting the conglomeration of people, implementation of curfews, closures of school and universities, government-mandated social distancing, and extensive contact tracing may have mitigated the exponential spread of COVID-19. Mandatory mask-wearing in public may have helped to prevent spread between asymptomatic carriers to susceptible individuals. A low population density may have also contributed to containing the virus.

scholarly journals Management of gonorrhoea in a hospital network: are we following best practice?

Sexual Health ◽  
2019 ◽  
Vol 16 (5) ◽  
pp. 523 ◽  
Author(s):  
Leong Shuen Loo ◽  
Kathryn Cisera ◽  
Tony M. Korman ◽  
Ian Woolley
Keyword(s):  
Public Health ◽  
Hiv Testing ◽  
Best Practice ◽  
Health Management ◽  
Retrospective Chart Review ◽  
Screening Tests ◽  
Contact Tracing ◽  
Large Hospital ◽  
Term Care ◽  
Sexually Transmissible Infections

Background Gonorrhoea is usually managed in community sexual health or general practice, but a proportion of cases present to hospital settings. In this study, we examined how gonorrhoea was managed through a large hospital network and what the implications may be for public health management. Methods: A retrospective chart review was performed of the management of patients with Neisseria gonorrhoeae infection diagnosed at a large Australian healthcare network from January 2015 to May 2018. Documentation rates of five parameters of care were assessed: (1) the presence (or absence) of previous sexually transmissible infections (STIs); (2) recent travel; (3) discussion of HIV testing; (4) contact tracing; and (5) public health notification. Results: In all, 110 cases (48 male, 62 female) were analysed. Most cases were in the 15–39 years age group; 98 cases (89%) were symptomatic, and 12 (11%) were screening tests. The most common presenting syndromes were pelvic inflammatory disease (32%; 31/98 symptomatic cases), urethritis (26%; 25/98) and epididymo-orchitis (13%; 13/98). None of the five parameters assessed were documented in most cases. Documentation was most likely to occur in patients admitted to hospital. When HIV testing was performed, no new cases of HIV were identified. Conclusion: Infections with gonorrhoea present on a regular basis to hospital practice, but overall management is suboptimal. Automated prompts for other recommended tests, including HIV testing when testing for other sexually transmissible diseases is ordered, may improve management. Better awareness of best practice is needed, which can be facilitated with ongoing education. However, the greatest benefit is likely achieved by linking patients back to community-based services, which are best placed to provide ongoing long-term care.

scholarly journals Intrinsic and realized generation intervals in infectious-disease transmission

2015 ◽  
Vol 282 (1821) ◽  
pp. 20152026 ◽  
Author(s):  
David Champredon ◽  
Jonathan Dushoff
Keyword(s):  
Public Health ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Interval Data ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Infectious Disease Transmission ◽  
Generation Interval ◽  
Public Health Strategies ◽  
The Relationship

The generation interval is the interval between the time when an individual is infected by an infector and the time when this infector was infected. Its distribution underpins estimates of the reproductive number and hence informs public health strategies. Empirical generation-interval distributions are often derived from contact-tracing data. But linking observed generation intervals to the underlying generation interval required for modelling purposes is surprisingly not straightforward, and misspecifications can lead to incorrect estimates of the reproductive number, with the potential to misguide interventions to stop or slow an epidemic. Here, we clarify the theoretical framework for three conceptually different generation-interval distributions: the ‘intrinsic’ one typically used in mathematical models and the ‘forward’ and ‘backward’ ones typically observed from contact-tracing data, looking, respectively, forward or backward in time. We explain how the relationship between these distributions changes as an epidemic progresses and discuss how empirical generation-interval data can be used to correctly inform mathematical models.

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