Clusters Of COVID-19 Cases Research Articles

LncRNAs expression profile in a family household cluster of COVID-19 patients.

More than 3 years after the start of SARS-CoV-2 pandemic, the molecular mechanisms behind the viral pathogenesis are still not completely understood. Long non-coding RNAs (lncRNAs), well-known players in viral infections, can represent prime candidates for patients' risk stratification. The purpose of the current study was to investigate the lncRNA profile in a family cluster of COVID-19 cases with different disease progression, during the initial wave of the pandemic and to evaluate their potential as biomarkers for COVID-19 evolution. LncRNA expression was investigated in nasopharyngeal swabs routinely collected for diagnosis. Distinct expression patterns of five lncRNAs (HOTAIR, HOTAIRM1, TMEVPG1, NDM29 and snaR) were identified in all the investigated cases, and they were associated with disease severity. Additionally, a significant increase in the expression of GAS5-family and ZFAS1 lncRNAs, which target factors involved in the inflammatory response, was observed in the sample collected from the patient with the most severe disease progression. An lncRNA prognostic signature was defined, opening up novel research avenues in understanding the interactions between lncRNAs and SARS-CoV-2.

Geographic Coterminous of COVID-19 Case and Vaccination Centers in the Greater Accra Metropolitan Area, Ghana

Ghana was the first country globally to receive about 600,000 Oxford-AstraZeneca vaccine doses from the WHO-backed Covax program in February 2021. Ghana’s initial plan was to vaccinate about two-thirds of its adult population by the end of October 2021. This necessitated the selection of vaccination centers across the country. This study analyzed vaccination centers location against other factors, including COVID-19 cases, population density, and poverty level to assess the efficiency of each center's location in Greater Accra Metropolitan Area (GAMA). Our study applied spatial-analytical techniques to access the spatial coterminous distribution of COVID-19 cases with vaccination centers within GAMA. Hotspot analysis of cases was performed using kernel density tool and compared to hotspots of vaccination centers. Nearest neighbor analysis and error differential ellipse were performed to access the spatial pattern of the distribution of COVID-19 vaccination centers within the GAMA. This study revealed three local clusters of COVID-19 cases in Tema East municipality, Ayawaso Central, and Ablekuma West; three vaccination hotspots, found in Ashaiman, Madina, and AyawasoCentral localities. COVID-19 case clusters are not coterminous with vaccination centers. The top three vaccination clusters were found in Ashiaman, Madina-La Nkwantanan, and Kpone-Katamanso which are inhabited by indigenous populations and also recorded the lowest COVID-19 cases. Tema East and Ayawaso East, which have the highest number of COVID-19 cases, recorded the lowest number of vaccination centers. We conclude that need-based allocation is lacking in the distribution of vaccination centers, which partly affects the equitable access and spatial distribution of COVID-19 vaccines.

Surface sampling for SARS-CoV-2 RNA in workplace outbreak settings in the UK, 2021-22.

To utilise environmental surface sampling to evaluate areas of SARS-CoV-2 contamination within workplaces to identify trends and improve local COVID-control measures. Surface sampling was undertaken at 12 workplaces that experienced a cluster of COVID-19 cases in the workforce between March 2021 and March 2022. 7.4% (61/829) of samples collected were positive for SARS-CoV-2 RNA by qPCR with only 1.8% (15/829) of samples identified with crossing threshold (Ct) values below 35.0. No sample returned whole genome sequence inferring RNA detected was degraded. Few workplace surface samples were positive for SARS-CoV-2 RNA and positive samples typically contained low levels of nucleic acid. Although these data may infer a low probability of fomite transmission within the workplace, Ct values may have been lower at the time of contamination. Workplace environmental sampling identified lapses in COVID-control measures within individual sites and showed trends through the pandemic.

Targeted Sanger Sequencing of a Cluster of COVID-19 Cases in the Surgical ICU of a Non-COVID Hospital: Lessons Learned.

Small clusters of infection due to SARS-CoV-2 in a non-COVID-19 healthcare facility can disrupt services. Here, we investigated a cluster of SARS-CoV-2 cases by targeted Sanger sequencing and clinical epidemiological methods in a non-COVID-19 super-specialty hospital. Epidemiological data were collected in a blinded manner using a proforma to find the risk factors associated with infection. Targeted Sanger sequencing of the spike protein receptor binding domain (RBD) coding region was performed on all the available real-time reverse transcription polymerase chain reaction (RT-PCR)-positive samples that included a patient, his mother, and 11 healthcare workers (HCWs) to determine any genomic variations in the samples from the cluster. All positive cases were due to the Delta variant. However, it detected a unique mutation, N501I, in the RBD region of the SARS-CoV-2 strains. The viral genome extracted from the mother's sample lacked the mutation, thus excluding her from the cluster and pointing out that the outbreak was nosocomial, leading to a focus on infection control measures. Though whole genome sequencing is more universally accepted, in this study, targeted sanger sequencing provided a rapid and cost-effective solution to correctly delineate between the actual cases that form the cluster and other community cases in a pandemic situation.

COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis.

This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic. Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well. The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns. Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.

SG-APSIC1056: Finding the right fit: Our experience in quantitative N95 respirator fit-testing

Objectives: Following a cluster of COVID-19 cases in a Singapore public hospital in April 2021, the local health authority mandated the use of N95 respirators in all inpatient wards. This increased the demand for N95 mask fit-testing to ensure that healthcare workers were donning respirators that fit their facial characteristics and hence provided protection through a good facial seal. The demand for fit-testing during the pandemic highlighted the scarcity of manpower and ergonomics concern, such as carpel tunnel syndrome experienced in long hours of qualitative fit-testing sessions. We evaluated the operational efficiency, cost-effectiveness, and difference in passing rate after the introduction of the quantitative method. Methods: Conventional qualitative fit-testing was conducted using manual pumping of a challenge agent, enabling the user to determine the fit of the respirator. The quantitative fit-testing protocol used a condensation particle counter (CPC) to measure the concentration of particles inside the mask and the atmosphere to determine the fit of respirator. The Occupational Safety and Health Administration (OSHA)–approved minimum fit factor of 100 was used as the criterion for a successful N95 respirator fit. Tubes used during quantitative fit-testing were reprocessed using thermal disinfection. Results: Quantitative mask fit-testing provided an objective numerical measure to assess adequate fit of N95 respirator, which provided users with confidence in the respirator fit. It addressed a manpower limitation issue because it did not require qualified trainers to conduct the test, and automation also prevented any potential occupational hazard from repeated actions required in qualitative fit-testing. An increase in the passing rate for N95 fit-testing from 94.5% to 95.5% was observed. However, the high cost of equipment, annual recalibration, and consumables must be considered. Conclusions: Quantitative N95 fit-testing, when adopted with careful consideration of its cost, is an approach to consider for hospital-wide fit-testing.

Prevalence of Otorhinolaryngological Manifestations of COVID-19 Infection during Third Wave and Comparison from Seasonal Flu and Allergic Rhinitis in a Tertiary Care Hospital

Background: Ever since the cluster of COVID-19 cases seen in Wuhan, China, the animal-origin virus has created havoc. These changes in clinical manifestations have been tracked in different waves, in different countries, also the virus behaving differently according to race and culture. To assess the prevalence of symptoms of COVID-19 infection during the third wave and compare it with seasonal flu and allergic rhinitis. Materials and Methods: A cross-sectional, comparative study was conducted at a tertiary hospital in Bareilly. A close-ended questionnaire was prepared to assess the otorhinolaryngological symptoms of COVID-19, seasonal flu, and allergic rhinitis. ENT findings were further confirmed by a suitable test. Results: A total of 145 tested positive for COVID-19 infection, 121 patients were diagnosed with seasonal flu, and 124 patients were diagnosed as cases of allergic rhinitis. Fever was the most common symptom in seasonal flu (100%) and COVID (93.8%) patients. Rhinorrhea (100%), lacrimation (100%), and sneezing (100%) were common symptoms in the allergic rhinitis group. A Chi-square test with Bonferroni correction showed a significant difference in the prevalence of symptoms in all three groups (P < 0.0001). Vaccination also contributed to less severity of COVID-19 disease. Conclusion: Involvement of otorhinolaryngology is considered a biomarker in all three groups with less frequent pulmonary features. It becomes necessary to correctly differentiate COVID-19 from the other two diseases for the proper treatment of patients.

Community-acquired Covid-19 Cluster among Non-Clinical Staff in a Teaching Hospital, Malaysia

A novel coronavirus, COVID-19 is responsible for the current outbreak of pneumonia. As of 16 September 2021, a total of 2,049,750 COVID-19 cases were recorded in Malaysia. In this study, we aimed to describe a cluster of COVID-19 cases among non-clinical staff in a teaching hospital in Kuala Lumpur Malaysia. We obtained epidemiological and clinical data from patients with confirmed COVID-19, via phone interviews and patient medical records. We performed chronological mapping of the cases to identify the possible period, mode of transmission, and cluster pattern. Three laboratory-confirmed COVID-19 cases were identified from the teaching hospital cluster. These three cases were linked epidemiologically to the religious gathering cluster in Malaysia. Seventy-four close contacts consisting of teaching hospital staff were quarantined. The median incubation period was four days (IQR 1-7). The need for intensified surveillance and a high level of alertness is vital to minimise the risk of widespread transmission in Malaysia.

Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran.

This study aimed at detecting space-time clusters of COVID-19 cases in Fars Province, Iran and at investigating their potential association with meteorological factors, such as temperature, precipitation and wind velocity. Time-series data including 53,554 infected people recorded in 26 cities from 18 February to 30 September 2020 together with 5876 meteorological records were subjected to the analysis. Applying a significance level of P<0.05, the analysis of space-time distribution of COVID-19 resulted in nine significant outbreaks within the study period. The most likely cluster occurred from 27 March to 13 July 2020 and contained 11% of the total cases with eight additional, secondary clusters. We found that the COVID-19 incidence rate was affected by high temperature (OR=1.64; 95% CI: 1.44-1.87), while precipitation and wind velocity had less effect (OR=0.84; 95% CI: 0.75-0.89 and OR=0.27; 95% CI: 0.14-0.51), respectively.

COVID-19 Mortality and Vaccine Coverage - Hong Kong Special Administrative Region, China, January 6, 2022-March 21, 2022.

On January 6, 2022, a cluster of COVID-19 cases* caused by the Omicron variant of SARS-CoV-2, the virus that causes COVID-19, was detected in Hong Kong Special Administrative Region, China (Hong Kong), resulting in the territory's fifth wave of COVID-19 cases (1). This wave peaked on March 4, 2022, with 8,764 COVID-19 cases per million population (2), resulting in a total of 1,049,959 cases and 5,906 COVID-19-associated deaths reported to the Hong Kong Department of Health during January 6-March 21, 2022.† Throughout this period, the COVID-19 mortality rate in Hong Kong (37.7 per million population) was among the highest reported worldwide since the COVID-19 pandemic began (3). Publicly available data on age-specific vaccination coverage in Hong Kong with a 2-dose primary vaccination series (with either Sinovac-CoronaVac [Sinovac], an inactivated COVID-19 viral vaccine, recommended for persons aged ≥3 years or BNT162b2 [Pfizer-BioNTech], an mRNA vaccine, for persons aged ≥5 years), as of December 23, 2021,§,¶ and COVID-19 mortality during January 6-March 21, 2022, were analyzed. By December 23, 2021, 67% of vaccine-eligible persons in Hong Kong had received ≥1 dose of a COVID-19 vaccine, 64% had received ≥2 doses, and 5% had received a booster dose. Among persons aged ≥60 years, these proportions were 52%, 49%, and 7%, respectively. Among those aged ≥60 years, vaccination coverage declined with age: 48% of persons aged 70-79 years had received ≥1 dose, 45% received ≥2 doses, and 7% had received a booster, and among those aged ≥80 years, 20%, 18%, and 2% had received ≥1 dose, ≥2 doses, and a booster dose, respectively. Among 5,906 COVID-19 deaths reported, 5,655 (96%) occurred in persons aged ≥60 years**; among these decedents, 3,970 (70%) were unvaccinated, 18% (1,023) had received 1 vaccine dose, and 12% (662) had received ≥2 doses. The overall rates of COVID-19-associated mortality among persons aged ≥60 years who were unvaccinated, who had received 1 COVID-19 vaccine dose, and who had received ≥2 vaccine doses were 10,076, 1,099, and 473 per million population, respectively; the risk for COVID-19-associated death among unvaccinated persons was 21.3 times that among recipients of 2-3 doses in this age group. The high overall mortality rate during the ongoing 2022 Hong Kong Omicron COVID-19 outbreak is being driven by deaths among unvaccinated persons aged ≥60 years. Efforts to identify and address gaps in age-specific vaccination coverage can help prevent high mortality from COVID-19, especially among persons aged ≥60 years.

Cluster of COVID-19 Cases in a Workplace: the First Cluster of a Workplace- related Outbreak in Malaysia, 2020

In the early stages of the COVID-19 crisis, there was no specific guideline for handling an outbreak if it occurred in a workplace. This study aimed to describe the first and one of the largest clusters linked to a business corporate in Malaysia. A descriptive analysis was conducted using surveillance data from the Petaling District Health Office notified between 28 Feb and 22 Mar 2020. All cases and contacts were identified through surveillance, epidemiological investigation, and laboratory investigation. The total number of confirmed cases and close contacts were 63 and 1,536, respectively. The respondents were mainly Malay, male, and the mean age was 46 years. Of the 63 positive cases, 48 (76%) were reported to have symptoms during the investigation, while the remaining 15 cases (24%) were asymptomatic. The main clinical manifestations were fever (52%), cough (37%), sore throat (27%) and shortness of breath (27%). The mean incubation period was 3.5 days. Due to the timely prevention and control measures carried out by the Petaling District Health Office, the chain of transmission was interrupted as the last case was reported on 19 Mar 2020.

Spatial diffusion of COVID-19 in Algeria during the third wave.

The differential geographic impact of the third wave of COVID-19 is unknown in Algeria. We thus analyze the spatiotemporal variations of cases and deaths of COVID-19 in Algeria, between January and mid-August 2021. Cases and deaths due to COVID-19 were aggregated at the wilaya (province) level. The space–time permutation scan statistic was applied retrospectively to identify spatial–temporal clusters of COVID-19 cases and deaths. We detected 14 spatio-temporal clusters of COVID-19 cases, with only one high risk cluster. Among the 13 low risk clusters, 7 clusters emerged before the start of the third wave and were mostly located in wilayas with lower population density compared to the clusters that emerged during the third wave. For deaths, the largest geographic low-risk cluster emerged in southern Algeria, between April and early July 2021. Northern and coastal wilayas should be prioritized when allocating resources and implementing various quarantine and isolation measures to slow viral transmission.

An exploration of factors characterising unusual spatial clusters of COVID-19 cases in the East Midlands region, UK: A geospatial analysis of ambulance 999 data

Complex interactions between physical landscapes and social factors increase vulnerability to emerging infections and their sequelae. Relative vulnerability to severe illness and/or death (VSID) depends on risk and extent of exposure to a virus and underlying health susceptibility. Identifying vulnerable communities and the regions they inhabit in real time is essential for effective rapid response to a new pandemic, such as COVID-19. In the period between first confirmed cases and the introduction of widespread community testing, ambulance records of suspected severe illness from COVID-19 could be used to identify vulnerable communities and regions and rapidly appraise factors that may explain VSID. We analyse the spatial distribution of more than 10,000 suspected severe COVID-19 cases using records of provisional diagnoses made by trained paramedics attending medical emergencies. We identify 13 clusters of severe illness likely related to COVID-19 occurring in the East Midlands of the UK and present an in-depth analysis of those clusters, including urban and rural dynamics, the physical characteristics of landscapes, and socio-economic conditions. Our findings suggest that the dynamics of VSID vary depending on wider geographic location. Vulnerable communities and regions occur in more deprived urban centres as well as more affluent peri-urban and rural areas. This methodology could contribute to the development of a rapid national response to support vulnerable communities during emerging pandemics in real time to save lives.

Rethinking the health implications of society-environment relationships in built areas: An assessment of the access to healthy and hazards index in the context of COVID-19

Urban environments have been evolving to mitigate threats to the health and wellbeing of societies for thousands of years, including establishing open spaces to combat bubonic plague, improving waste management in the 20th century, and more recently retrofitting urban landscapes with green space to promote physical exercise. In the context of the current COVID-19 pandemic there is a need to rethink how societies interact with space in built environments to prevent contagion at the same time as facilitating health behaviours, such as exercise. Previously, we examined the spatial relationship between features of urban landscapes that are commonly considered to be ‘hazardous’ and ‘healthy’ and unusual clusters of COVID-19 cases in the East Midlands of the UK using ambulance data. Here, we consider the nature of social engagement that these features of urban landscapes facilitate and identify society-environment interactions that may increase risk of exposure to the virus. In some cases, spaces that are commonly thought to promote health behaviour may increase exposure. Contagion hot-spots occur at the nexus of exposure and underlying susceptibility. The viral-host dynamics of infectious disease are changing. Now, as in past eras, societies are required to evolve and adapt to the new challenges presented by emerging infectious diseases in the modern world.

Spatiotemporal cluster analysis of COVID-19 and its relationship with environmental factors at the city level in mainland China.

This study sought to identify the spatial, temporal, and spatiotemporal clusters of COVID-19 cases in 366 cities in mainland China with the highest risks and to explore the possible influencing factors of imported risks and environmental factors on the spatiotemporal aggregation, which would be useful to the design and implementation of critical preventative measures. The retrospective analysis of temporal, spatial, and spatiotemporal clustering of COVID-19 during the period (January 15 to February 25, 2020) was based on Kulldorff’s time-space scanning statistics using the discrete Poisson probability model, and then the logistic regression model was used to evaluate the impact of imported risk and environmental factors on spatiotemporal aggregation. We found that the spatial distribution of COVID-19 cases was nonrandom; the Moran’s I value ranged from 0.017 to 0.453 (P < 0.001). One most likely cluster and three secondary likely clusters were discovered in spatial cluster analysis. The period from February 2 to February 9, 2020, was identified as the most likely cluster in the temporal cluster analysis. One most likely cluster and seven secondary likely clusters were discovered in spatiotemporal cluster analysis. Imported risk, humidity, and inhalable particulate matter PM2.5 had a significant impact on temporal and spatial accumulation, and temperature and PM10 had a low correlation with the spatiotemporal aggregation of COVID-19. The information is useful for health departments to develop a better prevention strategy and potentially increase the effectiveness of public health interventions.

The First COVID-19 Mortality in Batu Pahat, Johor: Lesson Learnt

COVID-19 had been declared as a global pandemic on 11 March 2020. This case report is about Severe Acute Respiratory Infection (SARI) due to COVID-19 (Corona Virus-19). A 68-yearold lady with underlying hypertension and congestive cardiac failure presented with fever and productive cough for 5 days duration. One week before her presentation, she had travelled to Kluang, Johor for a wedding gathering which was later found to have contributed to a cluster of COVID-19 cases. Otherwise, she denied any sick or known COVID-19 contact. Respiratory examination revealed left lower zonecrepitations. She was tachypnoeic on a high flow mask 15 L and hypotensive (BP 90/70 mm Hg) on arrival at the hospital. Chest radiograph showed bilateral asymmetrical consolidations. There was a presence of lymphopenia while her dengue serology result was negative. She was treated as SARI secondary to pneumonia with septic shock and required mechanical ventilation due to respiratory failure. Nasopharyngeal swab for SARS CoV-2 (Severe Acute Respiratory Syndrome Corona Virus-2) was taken as positive for COVID-19 on the third day of admission. Apart from supportive andintensive care measures, she was commenced on oral hydroxychloroquine, subcutaneous Interferon Beta and syrup lopinavir/ritonavir per local guidelines during that period. Unfortunately, she developed acute respiratory distress syndrome (ARDS) on day 3 of admission and passed away due to respiratory failure. Clinicians should pay attention to the COVID-19 critical disease profile and mortality risks. By identifying high-risk patients early, medical resources can be administered in an organized way and timely way to improve the efficacy of the healthcare services.

COVID-19: first long-term care facility outbreak in the Netherlands following cross-border introduction from Germany, March 2020

BackgroundThe Dutch province of Limburg borders the German district of Heinsberg, which had a large cluster of COVID-19 cases linked to local carnival activities before any cases were reported in the Netherlands. However, Heinsberg was not included as an area reporting local or community transmission per the national case definition at the time. In early March, two residents from a long-term care facility (LTCF) in Sittard, a Dutch town located in close vicinity to the district of Heinsberg, tested positive for COVID-19. In this study we aimed to determine whether cross-border introduction of the virus took place by analysing the LTCF outbreak in Sittard, both epidemiologically and microbiologically.MethodsSurveys and semi-structured oral interviews were conducted with all present LTCF residents by health care workers during regular points of care for information on new or unusual signs and symptoms of disease. Both throat and nasopharyngeal swabs were taken from residents suspect of COVID-19, based on regional criteria, for the detection of SARS-CoV-2 by Real-time Polymerase Chain Reaction. Additionally, whole genome sequencing was performed using a SARS-CoV-2 specific amplicon-based Nanopore sequencing approach. Moreover, twelve random residents were sampled for possible asymptomatic infections.ResultsOut of 99 residents, 46 got tested for COVID-19. Out of the 46 tested residents, nineteen (41%) tested positive for COVID-19, including 3 asymptomatic residents. CT-values for asymptomatic residents seemed higher compared to symptomatic residents. Eleven samples were sequenced, along with three random samples from COVID-19 patients hospitalized in the regional hospital at the time of the LTCF outbreak. All samples were linked to COVID-19 cases from the cross-border region of Heinsberg, Germany.ConclusionsSequencing combined with epidemiological data was able to virtually prove cross-border transmission at the start of the Dutch COVID-19 epidemic. Our results highlight the need for cross-border collaboration and adjustment of national policy to emerging region-specific needs along borders in order to establish coordinated implementation of infection control measures to limit the spread of COVID-19.

Non-intrusive wastewater surveillance for monitoring of a residential building for COVID-19 cases

Wastewater-based surveillance for SARS-CoV-2 has been used for the early warning of transmission or objective trending of the population-level disease prevalence. Here, we describe a new use-case of conducting targeted wastewater surveillance to complement clinical testing for case identification in a small community at risk of COVID-19 transmission. On 2 July 2020, a cluster of COVID-19 cases in two unrelated households residing on different floors in the same stack of an apartment building was reported in Singapore. After cases were conveyed to healthcare facilities and six healthy household contacts were quarantined in their respective apartments, wastewater surveillance was implemented for the entire residential block. SARS-CoV-2 was subsequently detected in wastewaters in an increasing frequency and concentration, despite the absence of confirmed COVID-19 cases, suggesting the presence of fresh case/s in the building. Phone interviews of six residents in quarantine revealed that no one was symptomatic (fever/respiratory illness). However, when nasopharyngeal swabs from six quarantined residents were tested by PCR tests, one was positive for SARS-CoV-2. The positive case reported episodes of diarrhea and the case's stool sample was also positive for SARS-CoV-2, explaining the SARS-CoV-2 spikes observed in wastewaters. After the case was conveyed to a healthcare facility, wastewaters continued to yield positive signals for five days, though with a decreasing intensity. This was attributed to the return of recovered cases, who had continued to shed the virus. Our findings demonstrate the utility of wastewater surveillance as a non-intrusive tool to monitor high-risk COVID-19 premises, which is able to trigger individual tests for case detection, highlighting a new use-case for wastewater testing.

COVID-19 and homelessness: when crises intersect

COVID-19 and homelessness: when crises intersect

Active COVID-19 clusters in Santa Catarina, Brazil, and workplace mobility trends

The application of spatial analysis to the study of human epidemiological data has gained great momentum in the last two decades. This article approaches the scan statistic for the detection of spatial-temporal clusters of COVID-19 cases in the State of Santa Catarina, Brazil. The aim is to apply the scan statistic to identify active clusters, determining their location, size, and order (priority). Organization of the descriptive based included COVID-19 cases from March 1 to August 31, 2020, available in the Santa Catarina State Open Data Portal. The vector base of the municipal limits and mesoregions in Santa Catarina and the population estimates for 2020 were obtained from the Brazilian Institute of Geography and Statistics (IBGE) website. The workplace mobility trend covariable was obtained from the document COVID-19: Report on Community Mobility in Google. Execution of the statistic considered the discrete Poisson model, supported by the prospective approach. The study's results evidenced the procedure's capacity to demarcate clusters, identifying 17 active clusters with the response variable and 18 active clusters after inclusion of the covariable, distributed throughout the state and predominantly on the coast and the Western region. The primary cluster was in Southern Santa Catarina. The workplace mobility trend covariable moderately influenced 38.89% of the clusters. The method proved to be efficient for understanding the epidemic's spatial distribution. This characterizes the scan statistic as a tool to support the execution of actions by policymakers, prioritizing areas most affected by the disease.