COVID-19 Hotspots Research Articles

Frequent Infection of Cats With SARS-CoV-2 Irrespective of Pre-Existing Enzootic Coronavirus Immunity, Brazil 2020.

Carnivores such as cats and minks are highly susceptible to SARS-CoV-2. Brazil is a global COVID-19 hot spot and several cases of human-to-cat transmission have been documented. We investigated the spread of SARS-CoV-2 by testing 547 domestic cats sampled between July-November 2020 from seven states in southern, southeastern, and northeastern Brazil. Moreover, we investigated whether immune responses elicited by enzootic coronaviruses affect SARS-CoV-2 infection in cats. We found infection with significantly higher neutralizing antibody titers against the Gamma variant of concern, endemic in Brazil during 2020, than against an early SARS-CoV-2 B.1 isolate (p<0.0001), validating the use of Gamma for further testing. The overall SARS-CoV-2 seroprevalence in Brazilian cats during late 2020 validated by plaque reduction neutralization test (PRNT90) was 7.3% (95% CI, 5.3-9.8). There was no significant difference in SARS-CoV-2 seroprevalence in cats between Brazilian states, suggesting homogeneous infection levels ranging from 4.6% (95% CI, 2.2-8.4) to 11.4% (95% CI, 6.7-17.4; p=0.4438). Seroprevalence of the prototypic cat coronavirus Feline coronavirus (FCoV) in a PRNT90 was high at 33.3% (95% CI, 24.9-42.5) and seroprevalence of Bovine coronavirus (BCoV) was low at 1.7% (95% CI, 0.2-5.9) in a PRNT90. Neutralizing antibody titers were significantly lower for FCoV than for SARS-CoV-2 (p=0.0001), consistent with relatively more recent infection of cats with SARS-CoV-2. Neither the magnitude of SARS-CoV-2 antibody titers (p=0.6390), nor SARS-CoV-2 infection status were affected by FCoV serostatus (p=0.8863). Our data suggest that pre-existing immunity against enzootic coronaviruses neither prevents, nor enhances SARS-CoV-2 infection in cats. High SARS-CoV-2 seroprevalence already during the first year of the pandemic substantiates frequent infection of domestic cats and raises concerns on potential SARS-CoV-2 mutations escaping human immunity upon spillback.

The Disruptive Effects of COVID-19 Pandemic on the Dynamics of EU Macroeconomic Indicators and on Essential Supply Chains

As evidence shows, in the early stages of the pandemic, medical services were overwhelmed by the large number of COVID-19 cases that far exceeded both initial predictions and logistic capacity. To decelerate and contain the spread of the virus, the European Union, like all other great economic powers, attempted to provide a symmetrical response. Thus, curfew measures were imposed in order to restrict mobility and isolate the population from the COVID-19 hotspots. These measures could be associated to widespread contagion effects in the economy, a deterioration of the public finances and a distortion of many supply chains, some of them critical for the EU. The main aim of this paper is to provide a more objective and comprehensive picture of how the European Union economic landscape has been affected by the coronavirus pandemic. In this regard, the author analyses, using some of the most relevant databases, the manner in which the COVID-19 pandemic has influenced, at a macroeconomic level, the dynamics of production, public spending, private investment, consumption and intra- and extra-EU trade. Further, the second aim was to analyse the directions proposed by the European Union for the post-pandemic economic recovery. Finally, the author underlines the need to focus more on the security of supply for essential materials in dealing with future crises, such as climate change. The research is mainly phenomenological in nature and, with the help of the positivist dimension, the author captured the most representative and useful correlations and causal relationships resulting from the analytical approach used in this paper.

Impact of the COVID-19 pandemic on the management and outcomes of emergency surgical patients: A retrospective cohort study.

The COVID-19 pandemic has led to drastic measures being implemented for the management of surgical patients across all health services worldwide, including the National Health Service in the United Kingdom. It is suspected that the virus has had a detrimental effect on perioperative morbidity and mortality. Therefore, the aim of this study was to assess the impact of the COVID-19 pandemic on these outcomes in emergency general surgical patients. Emergency general surgical admissions were included in this retrospective cohort study in one of the COVID-19 hotspots in the South East of England. The primary outcome was the 30-day mortality rate. Secondary outcomes included the length of stay in hospital, complication rate and severity grade and admission rates to the ITU. Of 123 patients, COVID-19 was detected in 12.2%. Testing was not carried out in 26%. When comparing COVID-positive to COVID-negative patients, the mean age was 71.8 + 8.8 vs. 50.7 + 5.7, respectively, and female patients accounted for 40.0 vs. 52.6%. The 30-day mortality rate was 26.7 vs. 3.9 (OR 6.49, p = 0.02), respectively. The length of stay in hospital was 20.5 + 22.2 vs. 7.7 + 9.8 (p < 0.01), the rate of complications was 80.0 vs. 23.7 (OR 12.9, p < 0.01), and the rate of admission to the ITU was 33.3 vs. 7.9% (OR 5.83, p = 0.01). This study demonstrates the detrimental effect of COVID-19 on emergency general surgery, with significantly worsened surgical outcomes.

Monitoring COVID-19 spread in Prague local neighborhoods based on the presence of SARS-CoV-2 RNA in wastewater collected throughout the sewer network

Many reports have documented that the presence of SARS-CoV-2 RNA in the influents of municipal wastewater treatment plants (WWTP) correlates with the actual epidemic situation in a given city. However, few data have been reported thus far on measurements upstream of WWTPs, i.e. throughout the sewer network. In this study, the monitoring of the presence of SARS-CoV-2 RNA in Prague wastewater was carried out at selected locations of the Prague sewer network from August 2020 through May 2021. Various locations such as residential areas of various sizes, hospitals, city center areas, student dormitories, transportation hubs (airport, bus terminal), and commercial areas were monitored together with four of the main Prague sewers. The presence of SARS-CoV-2 RNA was determined by reverse transcription – multiplex quantitative polymerase chain reaction (RT-mqPCR) after the precipitation of nucleic acids with PEG 8,000 and RNA isolation with TRIzol™ Reagent. The number of copies of the gene encoding SARS-CoV-2 nucleocapsid (N1) per liter of wastewater was compared with the number of officially registered COVID-19 cases in Prague. Although the data obtained by sampling wastewater from the major Prague sewers were more consistent than those obtained from the small sewers, the correlation between wastewater-based and clinical-testing data was also good for the residential areas with more than 7,000 registered inhabitants. It was shown that monitoring SARS-CoV-2 RNA in wastewater sampled from small sewers could identify isolated occurrences of COVID-19-positive cases in local neighborhoods. This can be very valuable while tracking COVID-19 hotspots within large cities.

A molecular surveillance-guided vector control response to concurrent dengue and West Nile virus outbreaks in a COVID-19 hotspot of Florida

Simultaneous dengue virus (DENV) and West Nile virus (WNV) outbreaks in Florida, USA, in 2020 resulted in 71 dengue virus serotype 1 and 86 WNV human cases. We hypothesized that we would find a number of DENV-1 positive mosquito pools, and that the distribution of these arbovirus-positive mosquito pools would be associated with those neighborhoods for which imported DENV cases have been recently reported in 2019 and 2020. We collected and screened Aedes aegypti, Ae. albopictus, Anopheles crucians, Culex coronator, Cx. nigripalpus, and Cx. quinquefasciatus mosquitoes from Miami-Dade County (Florida) for DENV and WNV by rRT-qPCR. Spatial statistical analyses were performed to capture positive mosquito pool distribution in relation to land use, human demography, environmental variables, mosquito trap placement and reported human travel associated DENV cases to guide future mosquito control outbreak responses. A rapid screen of 7,668 mosquitoes detected four DENV serotype 2 (DENV-2), nine DENV-4 and nine WNV-positive mosquito pools, which enabled swift and targeted abatement of trap sites by mosquito control. As expected, DENV-positive pools were in urban areas; however, we found WNV-positive mosquito pools in agricultural and recreational areas with no historical reports of WNV transmission. These findings demonstrate the importance of proactive arbovirus surveillance in mosquito populations to prevent and control outbreaks, particularly when other illnesses (e.g., COVID-19), which present with similar symptoms, are circulating concurrently. Growing evidence for substantial infection prevalence of dengue in mosquitoes in the absence of local index cases suggests a higher level of dengue endemicity in Florida than previously thought. This research was supported in part by U.S. Centers for Disease Control and Prevention (CDC) grant 1U01CK000510-03, Southeastern Regional Center of Excellence in Vector Borne Diseases Gateway Program.

Pediatric biorepository participation during the COVID-19 pandemic: predictors of enrollment and biospecimen donation

BackgroundPatient-level predictors of enrollment in pediatric biorepositories are poorly described. Especially in pandemic settings, understanding who is likely to enroll in a biorepository is critical to interpreting analyses conducted on biospecimens. We describe predictors of pediatric COVID-19 biorepository enrollment and biospecimen donation to identify gaps in COVID-19 research on pediatric biospecimens.MethodsWe compared data from enrollees and non-enrollees aged 0–25 years with suspected or confirmed COVID-19 infection who were approached for enrollment in the Massachusetts General Hospital pediatric COVID-19 biorepository between April 12, 2020, and May 28, 2020, from community or academic outpatient or inpatient settings. Demographic and clinical data at presentation to care were from automatic and manual chart extractions. Predictors of enrollment and biospecimen donation were assessed with Poisson regression models.ResultsAmong 457 individuals approached, 214 (47%) enrolled in the biorepository. A COVID-19 epidemiologic risk factor was recorded for 53%, and 15% lived in a US Centers for Disease Control and Prevention-defined COVID-19 hotspot. Individuals living in a COVID-19 hotspot (relative risk (RR) 2.4 [95% confidence interval (CI): 1.8–3.2]), with symptoms at presentation (RR 1.8 [95% CI: 1.2–2.7]), or admitted to hospital (RR 1.8 [95% CI: 1.2–2.8]) were more likely to enroll. Seventy-nine percent of enrollees donated any biospecimen, including 97 nasopharyngeal swabs, 119 oropharyngeal swabs, and 105 blood, 16 urine, and 16 stool specimens, respectively. Age, sex, race, ethnicity, and neighborhood-level socioeconomic status based on zip code did not predict enrollment or biospecimen donation.ConclusionsWhile fewer than half of individuals approached consented to participate in the pediatric biorepository, enrollment appeared to be representative of children affected by the pandemic. Living in a COVID-19 hotspot, symptoms at presentation to care and hospital admission predicted biorepository enrollment. Once enrolled, most individuals donated a biospecimen.

Tracking SARS-CoV-2 in urban wastewater samples during the first wave of COVID-19 epidemic in Lombardy (Italy)

PurposeThe value of SARS-CoV-2 monitoring in urban wastewater samples (WWS) for surveillance of virus spread at a population-wide level has been largely demonstrated. Aim of this study was to optimize an analytical workflow to detect SARS-CoV-2 RNA in WWS and to monitor SARS-CoV-2 spread during the first wave of COVID-19 epidemic (March–June 2020) in Lombardy, northern Italy.Methods & MaterialsThe workflow consisted in WWS concentration by using PEG-8000 precipitation, a modified RNA extraction (QIAamp MinElute Virus Spin Kit; QIAGEN) and a one-step real-time RT-PCR detecting two portions of the N gene of SARS-CoV-2. Composite 24-hour WWS were collected once a week at the inlet of 8 wastewater treatment plants (WWTPs) with an overall catchment of 2,276,000 inhabitants, located in representative COVID-19 hotspots in Lombardy, from the end of March to mid-June 2020. 107 WWS were obtained and analysed. SARS-CoV-2 RNA copies/L/WWS were multiplied by the flow rate of each WWTP (m3/day) and the obtained load (copies/day/1,000 people) was normalized to the number of inhabitants served by WWTPs.ResultsThe optimized workflow allowed to identify 1E+3 copies/mL of SARS-CoV-2 in concentrated WWS with a turnaround time of 8 hours. Overall, the presence of SARS-CoV-2 RNA was identified in 65/107 WWS (61%). The highest rate of positive WWS (78.7%; 26/33) was identified in the Bergamo province, that was the epicentre during the first wave of COVID-19 epidemic (March-June 2020) in Lombardy. The highest amount of SARS-CoV-2 RNA was identified in late March/early April, when the overall viral load reflecting the number of individuals shedding the virus ranged from 9.3E+10 copies/day/1,000 people to 8.2E+8 copies/day/1,000 people. Since the end of May, WWS tested negative to SARS-CoV-2 detection.ConclusionAccording to the epidemiological features of the first wave of SARS-CoV-2 epidemic in Lombardy, the highest amount of SARS-CoV-2 RNA was detected in WWS collected in the areas most affected by COVID-19 (i.e. Bergamo province). This optimized workflow of WWS surveillance can help assessing the real number of individuals – both symptomatic and asymptomatic – able to spread the virus and appraising the effect of preventive measures.

Expanded use of telemedicine for thyroid and parathyroid surgery in the COVID-19 era and beyond

BackgroundThe COVID-19 pandemic has greatly expanded the use of telemedicine in healthcare. Surgical thyroid and parathyroid diseases are uniquely suited for comprehensive telemedicine. The objective of this study was to compare the safety and efficacy of telemedicine with in-person preoperative visits in patients undergoing thyroid and parathyroid surgery. MethodsProspective cohort study of patients undergoing thyroid and parathyroid surgery at a tertiary care center in a COVID-19 hotspot from March 2020 to October 2020. Patients were divided into a telemedicine cohort, with preoperative consultation and surgical decision-making conducted via telemedicine, and a conventional in-person cohort. ResultsOf 94 patients, 28 were enrolled in the telemedicine cohort and 66 were enrolled in the conventional cohort. Telemedicine patients were more likely to have parathyroid disease (50% versus 24%, p = 0.02) compared with the conventional cohort, but there was no significant difference in surgery for malignancy (43% versus 56%, p = 0.27). There were no significant differences in surgical outcomes or postoperative complications between cohorts, including intraoperative blood loss (19.4 mL versus 35.5 mL, p = 0.06), postoperative length of stay (1.3 days versus 1.2 days, p = 0.93), persistent hypocalcemia (3.6% versus 0%, p = 0.30), and true vocal fold paresis (0% versus 4.5%, p = 0.55). ConclusionsWith careful selection, many patients undergoing thyroid and parathyroid surgery may be safely treated using comprehensive telemedicine.

Water and wastewater digital surveillance for monitoring and early detection of the COVID-19 hotspot: industry 4.0.

There are a high number of COVID-19 cases per capita in the world that goes undetected including clinical diseases compatible with COVID-19. While the presence of the COVID-19 in untreated drinking water is possible, it is yet to be detected in the drinking-water supplies. COVID-19 viral fragments have been found in excrete, this call for wastewater monitoring and analysis (wastewater surveillance) of the potential health risk. This raises concern about the potential of the SARS-CoV-2 transmission via the water systems. The economic limits on the medical screening for the SARS-CoV-2 or COVID-19 worldwide are turning to wastewater-based epidemiology as great potential tools for assessing and management of the COVID-19 pandemic. Surveillance and tracking of the pathogens in the wastewater are key to the early warning system and public health strategy monitoring of the COVID-19. Currently, RT-qPCR assays is been developed for SARS-CoV-2 RNA specimen clinical testing and detection in the water system. Convectional wastewater treatment methods and disinfection are expected to eradicate the SAR-CoV-2. Chlorine, UV radiation, ozone, chloramine is been used to inactivate and disinfect the water treatment system against the SARS-CoV-2. Water management and design of the water infrastructure require major changes to accommodate climate change, water cycle, reimaging of digitalization, infrastructure and privacy protection. The water digital revolution, biosensors and nanoscale, contact tracing, knowledge management can accelerate with disruption of the COVID-19 outbreak (water-health-digital nexus).

COVID-19 hot-spot strategy: a special innovation in pandemic response, Oyo State Nigeria

BackgroundCOVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into a pandemic. Oyo state, Nigeria with a population of 9,233,010, recorded the first case of COVID-19 on the 12th of March 2020 and it is among the highest contributing States to the nation’s burden of the disease with 3267 confirmed cases, including 40 deaths as of date, with an overall test positivity rate of 18.1%, far higher compared to the National average within a limited period from recorded index case. A ‘Hotspot strategy’ was designed by the Presidential Task Force on COVID-19 and Oyo State was selected to implement the strategy through upscaling case detection, isolation and treatment, quarantine of contacts and strengthening public health and social measures.MethodsWe used a descriptive cross-sectional survey of 3 identified hotspot Local Government Areas (LGAs) in Oyo State using mobile phones under Surveillance, Outbreak Response Management and Analysis System (SORMAS) platform to collect data from October to December 2020. Interventions comprised of enhanced active case search, contact line listing, contact investigation, and contact follow-up as well as to facilitate data collection and entry, community sensitization and management of alert/rumors. Baseline information and that after the 3-month period was then analyzed with the descriptive statistics presented.ResultsThe implementation of the hotspot strategy was shown to have had a major impact in Irepo LGA, where more than a 100% increase in samples tested, confirmed cases, contacts listed and contacts followed were recorded, while there were no significance changes noticed in Ibadan North and Lagelu LGAs. However, test positivity rates among contacts were found to be quite high in Ibadan North LGA (48%), compared to the other two, even though Lagelu LGA (5.7%) tested more contacts than Ibadan North.ConclusionThe observed increase in number of samples tested, cases confirmed, contact listed and investigated as well as test positivity rate in the 3 LGAs after the intervention implies that the hotspot strategy can be said to have contributed positively to the sensitivity of COVID-19 surveillance in Oyo State, Nigeria. This implies that strengthening this ‘hotspot strategy’ may be a key area of focus to improve COVID-19 surveillance sensitivity and response and in turn may help in breaking the transmission and bringing the pandemic to a halt.

Comparison of Complications after Coronavirus Disease and Seasonal Influenza, South Korea.

We conducted a retrospective cohort study using claims data to determine the number and types of complications from coronavirus disease (COVID-19) that patients experience and which patients are more vulnerable to those complications compared with complications in patients with influenza. Among the cohort, 19.6% of COVID-19 patients and 28.5% of influenza patients had >1 new complication. In most complications, COVID-19 patients had lower or similar relative risk compared with influenza patients; exceptions were hair loss, heart failure, mood disorder, and dementia. Young to middle-aged adult COVID-19 patients and patients in COVID-19 hotspots had a higher risk for complications. Overall, COVID-19 patients had fewer complications than influenza patients, but caution is necessary in high-risk groups. If the fatality rate for COVID-19 is reduced through vaccination, management strategies for this disease could be adapted, similar to those for influenza management, such as easing restrictions on economic activity or requirements for close-contact isolation.

Revealing Critical Characteristics of Mobility Patterns in New York City During the Onset of COVID-19 Pandemic

New York has become one of the worst-affected COVID-19 hotspots and a pandemic epicenter due to the ongoing crisis. This paper identifies the impact of the pandemic and the effectiveness of government policies on human mobility by analyzing multiple datasets available at both macro and micro levels for New York City. Using data sources related to population density, aggregated population mobility, public rail transit use, vehicle use, hotspot and non-hotspot movement patterns, and human activity agglomeration, we analyzed the inter-borough and intra-borough movement for New York City by aggregating the data at the borough level. We also assessed the internodal population movement amongst hotspot and non-hotspot points of interest for the month of March and April 2020. Results indicate a drop of about 80% in people’s mobility in the city, beginning in mid-March. The movement to and from Manhattan showed the most disruption for both public transit and road traffic. The city saw its first case on March 1, 2020, but disruptions in mobility can be seen only after the second week of March when the shelter in place orders was put in effect. Owing to people working from home and adhering to stay-at-home orders, Manhattan saw the largest disruption to both inter- and intra-borough movement. But the risk of spread of infection in Manhattan turned out to be high because of higher hotspot-linked movements. The stay-at-home restrictions also led to an increased population density in Brooklyn and Queens as people were not commuting to Manhattan. Insights obtained from this study would help policymakers better understand human behavior and their response to the news and governmental policies.

COVID-19 vaccine inequity, dependency, and production capability in low-income and middle-income countries: the case of Bangladesh

COVID-19 vaccine inequity, dependency, and production capability in low-income and middle-income countries: the case of Bangladesh

"How Can We Track COVID-19 Hotspots and Prevent its Spread?"

"How Can We Track COVID-19 Hotspots and Prevent its Spread?"

A study on geospatially assessing the impact of COVID-19 in Maharashtra, India

The emergence of 2019 novel corona virus disease (COVID-19) raised global health concerns throughout the world. It has become a major challenge for healthcare personnel and researchers throughout the world to efficiently track and prevent the transmission of this virus. In this paper, the role of geographic information system (GIS) based spatial models for tracking the spread of COVID-19 and discovery of testing centres in Maharashtra, India was studied. The datasets collected from diverse sources were geocoded to make it geospatially compatible. A three-tiered framework was proposed to practically realize the impact of COVID-19 in a cartographic fashion. Initially, choropleth maps labeled with testing centres, number of confirmed cases and casualties were visualized in a district-wise manner. Heatmaps for visualizing the spatial density of confirmed cases and casualties were presented. The visualization of spatial K-means clustering for optimal value of “k” estimated using the heuristic-based Elbow method was provided along with zonal analysis of the districts. Map showing spatial autocorrelation was also presented to identify spatial hotspots and coldspots. The districts of Pune and Thane reported respective z-scores of 3.424 and 3.347 along with p-values of 0.006 and 0.001 respectively. It was inferred from the generated results that Pune and Thane districts in Maharashtra were identified as COVID-19 hotspots. Based upon this analysis, certain effective mitigation strategies can be devised in order to check the uncontrolled spread of COVID-19 in the identified hotspot areas.

Harnessing Virtual Mom Power: Process and Outcomes of a Pilot Telehealth Adaptation of a Multifamily, Attachment-Based Intervention

ABSTRACT The COVID-19 pandemic and mitigation strategies amplified racial and income-based health disparities, profoundly shifted family life, and altered delivery systems for support services. We report pilot data from a telehealth adaptation of Mom Power, an evidence-based, attachment-informed multifamily preventive intervention (clinicaltrials.gov: NCT04241913). We implemented an adaption of Virtual Mom Power (VMP) with a group of economically marginalized, predominantly Black mothers and their young children (n = 9) in New Orleans, an early COVID-19 hotspot with an entrenched history of structural racism and trauma. We outline our approach to adaptation of curriculum and service delivery, using a trauma-informed lens. Maternal reports of maternal and child functioning from pre- to post-intervention were consistent with improvements in maternal depressive and post-traumatic stress symptoms and child competence, comparable to outcomes from in-person trials. Feasibility and acceptability data were strong. Preliminary results and reflections on process suggest that telehealth service delivery of a multifamily preventive intervention, with attention to decreasing barriers to online access and consideration of culture and context, facilitated engagement while maintaining fidelity and effects on intervention targets. Future research using larger samples, randomized controlled design, and multi-method assessment should continue to guide dissemination of reflective, group-based telehealth parenting programs.

Analyzing the Implications of COVID-19 Pandemic through an Intelligent-Computing Technique

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the World Health Organization (WHO) on March 11, 2020. COVID-19 has already affected more than 211 nations. In such a bleak scenario, it becomes imperative to analyze and identify those regions in Saudi Arabia that are at high risk. A preemptive study done in the context of predicting the possible COVID-19 hotspots would facilitate in the implementation of prompt and targeted countermeasures against SARS-CoV-2, thus saving many lives. Working towards this intent, the present study adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, for assessing the risk of COVID-19 in different regions of Saudi Arabia. AHP gives the ability to measure the risks numerically. Moreover, numerical assessments are always effective and easy to understand. Hence, this research endeavour employs Fuzzy based computational method of decision making for its empirical analysis. Findings in the proposed paper suggest that Riyadh and Makkah are the most susceptible regions, implying that if sustained and focused preventive measures are not introduced at the right juncture, the two cities could be the worst afflicted with the infection. The results obtained through Fuzzy based computational method of decision making are highly corroborative and would be very useful for categorizing and assessing the current COVID-19 situation in the Kingdom of Saudi Arabia. More specifically, identifying the cities that are likely to be COVID-19 hotspots would help the country’s health and medical fraternity to reinforce intensive containment strategies to counter the ills of the pandemic in such regions.

Insights From the Author: Emily Mendenhall

How an Iowa Summer Resort Region Became a Covid-19 Hot Spot was first published on Vox.com and is reprinted here with permission from the authors, Vox.com, and Vox Media, LLC. The original article (with additional links) can be accessed at URL:&amp;lt;https://www.vox.com/2020/8/8/21357625/covid-19-iowa-lakes-okoboji-kim-reynolds-masks&amp;gt;. Photos by David Thoreson were added for this reprint and were not included in the original publication.

Jamal Rifi: family doctor building bridges in the community

During Australia's COVID-19 lockdown in 2021, family doctor Jamal Rifi came up with an unusual plan to encourage the remainder of his urban community of Sydney, NSW, Australia, to come forward for COVID-19 vaccination. “I realised we needed to build on our community advocacy work by using an inflatable vaccination facility that could be transported easily into the heart of urban communities that had become COVID-19 hotspots, such as my own suburb of Belmore in southwest Sydney”, Rifi explains. More recently, he has used the vaccination facility to help kick-start a COVID-19 booster programme, and from Jan 10, 2022, has also used it at a local bowling club to serve as a vaccination facility for children aged 5–11 years.

Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.