Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 311
Filter
Add filters

Document Type
Year range
1.
PLoS One ; 17(1): e0261756, 2022.
Article in English | MEDLINE | ID: covidwho-1613356

ABSTRACT

BACKGROUND: Worldwide, COVID-19 outbreaks in nursing homes have often been sudden and massive. The study investigated the role SARS-CoV-2 virus spread in nearby population plays in introducing the disease in nursing homes. MATERIAL AND METHODS: This was carried out through modelling the occurrences of first cases in each of 943 nursing homes of Auvergne-Rhône-Alpes French Region over the first epidemic wave (March-July, 2020). The cumulative probabilities of COVID-19 outbreak in the nursing homes and those of hospitalization for the disease in the population were modelled in each of the twelve Départements of the Region over period March-July 2020. This allowed estimating the duration of the active outbreak period, the dates and heights of the peaks of outbreak probabilities in nursing homes, and the dates and heights of the peaks of hospitalization probabilities in the population. Spearman coefficient estimated the correlation between the two peak series. RESULTS: The cumulative proportion of nursing homes with COVID-19 outbreaks was 52% (490/943; range: 22-70% acc. Département). The active outbreak period in the nursing homes lasted 11 to 21 days (acc. Département) and ended before lockdown end. Spearman correlation between outbreak probability peaks in nursing homes and hospitalization probability peaks in the population (surrogate of the incidence peaks) was estimated at 0.71 (95% CI: [0.66; 0.78]). CONCLUSION: The modelling highlighted a strong correlation between the outbreak in nursing homes and the external pressure of the disease. It indicated that avoiding disease outbreaks in nursing homes requires a tight control of virus spread in the surrounding populations.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Nursing Homes/trends , Communicable Disease Control/methods , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , France/epidemiology , Humans , Pandemics , SARS-CoV-2/pathogenicity
2.
J Med Virol ; 93(12): 6433-6436, 2021 12.
Article in English | MEDLINE | ID: covidwho-1557694

ABSTRACT

Lassa fever, caused by the Lassa virus of the Arenaviruses family, is a re-emerging public health concern that has led to 300,000 infections and 5000 deaths annually in Africa. Highly prevalent in Sierra Leone, Liberia, Guinea, Nigeria, Côte d'lvoire, Ghana, Togo, and Benin, patients infected with the virus can manifest with cough, sore throat, headache, nausea, and vomiting among other symptoms. Coexisting with the coronavirus disease 2019 (COVID-19) pandemic and its impacts, cases of Lassa fever in the African population have been reported to decrease due to hesitancy in visiting clinics that leads to unreported cases-all contributing to a silent outbreak in West Africa. Thus, to overcome current burdens, gaps, and challenges caused by Lassa fever amidst COVID-19 in Africa, various recommendations for efficient control of transmission, measures for disease containment, and strategies to correct misperceptions were made.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Lassa Fever/epidemiology , Lassa Fever/prevention & control , Patient Acceptance of Health Care/statistics & numerical data , Africa, Western/epidemiology , COVID-19/diagnosis , Disease Outbreaks/statistics & numerical data , Humans , Lassa Fever/diagnosis , Lassa virus , Molecular Diagnostic Techniques , Public Health , SARS-CoV-2 , Viral Vaccines
3.
Int J Prison Health ; ahead-of-print(ahead-of-print)2021 08 03.
Article in English | MEDLINE | ID: covidwho-1501266

ABSTRACT

PURPOSE: In this work, the authors present some of the key results found during early efforts to model the COVID-19 outbreak inside a UK prison. In particular, this study describes outputs from an idealised disease model that simulates the dynamics of a COVID-19 outbreak in a prison setting when varying levels of social interventions are in place, and a Monte Carlo-based model that assesses the reduction in risk of case importation, resulting from a process that requires incoming prisoners to undergo a period of self-isolation prior to admission into the general prison population. DESIGN/METHODOLOGY/APPROACH: Prisons, typically containing large populations confined in a small space with high degrees of mixing, have long been known to be especially susceptible to disease outbreaks. In an attempt to meet rising pressures from the emerging COVID-19 situation in early 2020, modellers for Public Health England's Joint Modelling Cell were asked to produce some rapid response work that sought to inform the approaches that Her Majesty's Prison and Probation Service (HMPPS) might take to reduce the risk of case importation and sustained transmission in prison environments. FINDINGS: Key results show that deploying social interventions has the potential to considerably reduce the total number of infections, while such actions could also reduce the probability that an initial infection will propagate into a prison-wide outbreak. For example, modelling showed that a 50% reduction in the risk of transmission (compared to an unmitigated outbreak) could deliver a 98% decrease in total number of cases, while this reduction could also result in 86.8% of outbreaks subsiding before more than five persons have become infected. Furthermore, this study also found that requiring new arrivals to self-isolate for 10 and 14 days prior to admission could detect up to 98% and 99% of incoming infections, respectively. RESEARCH LIMITATIONS/IMPLICATIONS: In this paper we have presented models which allow for the studying of COVID-19 in a prison scenario, while also allowing for the assessment of proposed social interventions. By publishing these works, the authors hope these methods might aid in the management of prisoners across additional scenarios and even during subsequent disease outbreaks. Such methods as described may also be readily applied use in other closed community settings. ORIGINALITY/VALUE: These works went towards informing HMPPS on the impacts that the described strategies might have during COVID-19 outbreaks inside UK prisons. The works described herein are readily amendable to the study of a range of addition outbreak scenarios. There is also room for these methods to be further developed and built upon which the timeliness of the original project did not permit.


Subject(s)
COVID-19/prevention & control , Disaster Planning/organization & administration , Disease Outbreaks/prevention & control , Prisoners/statistics & numerical data , Prisons/organization & administration , COVID-19/epidemiology , Disease Outbreaks/statistics & numerical data , Forecasting , Health Personnel/education , Humans , United Kingdom
4.
PLoS One ; 16(10): e0248325, 2021.
Article in English | MEDLINE | ID: covidwho-1496338

ABSTRACT

BACKGROUND: Since the beginning of the COVID-19 outbreak, many pharmaceutical companies have been racing to develop a safe and effective COVID-19 vaccine. Simultaneously, rumors and misinformation about COVID-19 are still widely spreading. Therefore, this study aimed to investigate the prevalence of COVID-19 misinformation among the Yemeni population and its association with vaccine acceptance and perceptions. METHODS: A cross-sectional online survey was conducted in four major cities in Yemen. The constructed questionnaire consisted of four main sections (sociodemographic data, misinformation, perceptions (perceived susceptibility, severity, and worry), and vaccination acceptance evaluation). Subject recruitment and data collection were conducted online utilizing social websites and using the snowball sampling technique. Descriptive and inferential analyses were performed using SPSS version 27. RESULTS: The total number of respondents was 484. Over 60% of them were males and had a university education. More than half had less than 100$ monthly income and were khat chewers, while only 18% were smokers. Misinformation prevalence ranged from 8.9% to 38.9%, depending on the statement being asked. Men, university education, higher income, employment, and living in urban areas were associated with a lower misinformation level (p <0.05). Statistically significant association (p <0.05) between university education, living in urban areas, and being employed with perceived susceptibility were observed. The acceptance rate was 61.2% for free vaccines, but it decreased to 43% if they had to purchase it. Females, respondents with lower monthly income, and those who believed that pharmaceutical companies made the virus for financial gains were more likely to reject the vaccination (p <0.05). CONCLUSION: The study revealed that the acceptance rate to take a vaccine was suboptimal and significantly affected by gender, misinformation, cost, and income. Furthermore, being female, non-university educated, low-income, and living in rural areas were associated with higher susceptibility to misinformation about COVID-19. These findings show a clear link between misinformation susceptibility and willingness to vaccinate. Focused awareness campaigns to decrease misinformation and emphasize the vaccination's safety and efficacy might be fundamental before initiating any mass vaccination in Yemen.


Subject(s)
COVID-19 , Disease Outbreaks , Vaccination Refusal , Vaccination , Adult , COVID-19/prevention & control , COVID-19/psychology , COVID-19 Vaccines/administration & dosage , Communication , Cross-Sectional Studies , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Female , Humans , Male , Socioeconomic Factors , Surveys and Questionnaires , Vaccination/psychology , Vaccination/statistics & numerical data , Vaccination Refusal/psychology , Vaccination Refusal/statistics & numerical data , Yemen/epidemiology
5.
Medicine (Baltimore) ; 100(28): e26634, 2021 Jul 16.
Article in English | MEDLINE | ID: covidwho-1494087

ABSTRACT

ABSTRACT: Emergency departments (EDs) are on the frontline of the coronavirus disease (COVID-19) outbreak. To resolve the abrupt overloading of COVID-19-suspected patients in a community, each ED needs to respond in various ways. In our hospital, we increased the isolation beds through temporary remodeling and by performing in-hospital COVID-19 polymerase chain reaction testing rather than outsourcing them. The aim of this study was to verify the effects of our response to the newly developed viral outbreak.The medical records of patients who presented to an ED were analyzed retrospectively. We divided the study period into 3: pre-COVID-19, transition period of response (the period before fully implementing the response measures), and post-response (the period after complete response). We compared the parameters of the National Emergency Department Information System and information about isolation and COVID-19.The number of daily ED patients was 86.8 ±â€Š15.4 in the pre-COVID-19, 36.3 ±â€Š13.6 in the transition period, and 67.2 ±â€Š10.0 in the post-response period (P < .001). The lengths of stay in the ED were significantly higher in transition period than in the other periods [pre-COVID-19 period, 219.0 (121.0-378.0) min; transition period, 301 (150.0-766.5) min; post-response period, 281.0 (114.0-575.0) min; P < .001]. The ratios of use of an isolation room and fever (≥37.5°C) were highest in the post-response period [use of isolation room: pre-COVID-19 period, 0.6 (0.7%); transition period, 1.2 (3.3%); post-response period, 16.1 (24.0%); P < .001; fever: pre-COVID-19 period, 14.8(17.3%); transition period, 6.8 (19.1%); post-response period, 14.5 (21.9%), P < .001].During an outbreak of a novel infectious disease, increasing the number of isolation rooms in the ED and applying a rapid confirmation test would enable the accommodation of more suspected patients, which could help reduce the risk posed to the community and thus prevent strain on the local emergency medical system.


Subject(s)
COVID-19 , Disease Outbreaks/statistics & numerical data , Emergency Medical Services/statistics & numerical data , Emergency Service, Hospital/statistics & numerical data , Infection Control/statistics & numerical data , Adult , Aged , Continuity of Patient Care/statistics & numerical data , Female , Humans , Male , Middle Aged , Patient Isolation/statistics & numerical data , Republic of Korea , Retrospective Studies , SARS-CoV-2
6.
Epidemiol Infect ; 149: e234, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1492957

ABSTRACT

Poultry contact is a risk factor for zoonotic transmission of non-typhoidal Salmonella spp. Salmonella illness outbreaks in the United States are identified by PulseNet, the national laboratory network for enteric disease surveillance. During 2020, PulseNet observed a 25% decline in the number of Salmonella clinical isolates uploaded by state and local health departments. However, 1722 outbreak-associated Salmonella illnesses resulting from 12 Salmonella serotypes were linked to contact with privately owned poultry, an increase from all previous years. This report highlights the need for continued efforts to prevent backyard poultry-associated outbreaks of Salmonella as ownership increases in the United States.


Subject(s)
COVID-19/epidemiology , Disease Outbreaks/statistics & numerical data , Poultry/microbiology , Salmonella Infections/epidemiology , Zoonoses/epidemiology , Animals , Humans , SARS-CoV-2 , Salmonella/isolation & purification , Salmonella Infections/microbiology , Salmonella Infections/transmission , Serogroup , United States/epidemiology , Zoonoses/microbiology , Zoonoses/transmission
7.
Endocrinol Metab (Seoul) ; 36(5): 1142-1146, 2021 10.
Article in English | MEDLINE | ID: covidwho-1485221

ABSTRACT

It has been suggested that the coronavirus disease 2019 (COVID-19) pandemic has had a negative impact on glycemic control in patients with type 2 diabetes mellitus (T2DM). However, no study has examined yearly trends in glycated hemoglobin (HbA1c) levels after the start of the COVID-19 outbreak. Here, we performed a retrospective analysis of HbA1c concentrations during the early period of the COVID-19 outbreak (COVID-19 cohort) and then compared the yearly trend in the mean HbA1c level, along with fluctuations in HbA1c levels, with those during previous years (non-COVID-19 cohorts). We observed that the mean HbA1c level in patients with T2DM increased during the first 6 months of the COVID-19 outbreak. After 6 months, HbA1c levels in the COVID-19 cohort returned to levels seen in the non-COVID-19 cohorts. The data suggest that vulnerable patients with T2DM should be monitored closely during the early period of a pandemic to ensure they receive appropriate care.


Subject(s)
COVID-19/epidemiology , Diabetes Mellitus, Type 2/blood , Glycated Hemoglobin A/analysis , Glycemic Control/trends , Adult , Blood Glucose/analysis , COVID-19/diagnosis , COVID-19/virology , Case-Control Studies , Diabetes Mellitus, Type 2/epidemiology , Disease Outbreaks/statistics & numerical data , Female , Humans , Male , Retrospective Studies , SARS-CoV-2/genetics , Time Factors
8.
Hong Kong Med J ; 26(3): 176-183, 2020 06.
Article in English | MEDLINE | ID: covidwho-1468777

ABSTRACT

INTRODUCTION: This study evaluated the preparedness of family doctors during the early phase of the coronavirus disease 2019 (COVID-19) outbreak in Hong Kong. METHODS: All members of the Hong Kong College of Family Physicians were invited to participate in a cross-sectional online survey using a 20-item questionnaire to collect information on practice preparedness for the COVID-19 outbreak through an email followed by a reminder SMS message between 31 January 2020 and 3 February 2020. RESULTS: Of 1589 family doctors invited, 491 (31%) participated in the survey, including 242 (49%) from private sector. In all, 98% surveyed doctors continued to provide clinical services during the survey period, but reduced clinic service demands were observed in 45% private practices and 24% public clinics. Almost all wore masks during consultation and washed hands between or before patient contact. Significantly more private than public doctors (80% vs 26%, P<0.001) experienced difficulties in stocking personal protective equipment (PPE); more public doctors used guidelines to manage suspected patients. The main concern of the respondents was PPE shortage. Respondents appealed for effective public health interventions including border control, quarantine measures, designated clinic setup, and public education. CONCLUSION: Family doctors from public and private sectors demonstrated preparedness to serve the community from the early phase of the COVID-19 outbreak with heightened infection control measures and use of guidelines. However, there is a need for support from local health authorities to secure PPE supply and institute public health interventions.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Disease Outbreaks/prevention & control , Family Practice/organization & administration , Health Care Surveys/methods , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Surveys and Questionnaires , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques/methods , Clinical Laboratory Techniques/statistics & numerical data , Communicable Disease Control/methods , Coronavirus Infections/diagnosis , Disease Outbreaks/statistics & numerical data , Female , Hong Kong/epidemiology , Humans , Male , Outcome Assessment, Health Care , Physicians, Family/statistics & numerical data
10.
Am J Public Health ; 111(8): 1534-1541, 2021 08.
Article in English | MEDLINE | ID: covidwho-1456158

ABSTRACT

Objectives. To empirically evaluate the relationship between presence of a state or federal prison and COVID-19 case and death counts. Methods. We merged data on locations of federal and state prisons and of local and county jails with daily case and death counts in the United States. We used a selection-on-observables design to estimate the correlation between prisons and COVID-19 spread, controlling for known correlates of COVID-19. Results. We found empirical evidence that the presence and capacities of prisons are strong correlates of county-level COVID-19 case counts. The presence of a state or federal prison in a county corresponded with a 9% increase in the COVID-19 case count during the first wave of the pandemic, ending July 1, 2020. Conclusions. Our results suggest that the public health implications of these facilities extend beyond the health of employees and incarcerated individuals, and policymakers should explicitly consider the public health concerns posed by these facilities when developing pandemic-response policy.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Communicable Disease Control/organization & administration , Disease Outbreaks/statistics & numerical data , Prisoners/statistics & numerical data , Prisons/statistics & numerical data , Humans , United States
11.
Am J Epidemiol ; 190(4): 611-620, 2021 04 06.
Article in English | MEDLINE | ID: covidwho-1447566

ABSTRACT

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.


Subject(s)
Disease Outbreaks/statistics & numerical data , Infections/epidemiology , Basic Reproduction Number , Global Health , Humans , Morbidity/trends , Software
12.
MMWR Morb Mortal Wkly Rep ; 70(39): 1372-1373, 2021 Oct 01.
Article in English | MEDLINE | ID: covidwho-1444554

ABSTRACT

CDC recommends universal indoor masking by students, staff members, faculty, and visitors in kindergarten through grade 12 (K-12) schools, regardless of vaccination status, to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 (1). Schools in Maricopa and Pima Counties, which account for >75% of Arizona's population (2), resumed in-person learning for the 2021-22 academic year during late July through early August 2021. In mid-July, county-wide 7-day case rates were 161 and 105 per 100,000 persons in Maricopa and Pima Counties, respectively, and 47.6% of Maricopa County residents and 59.2% of Pima County residents had received at least 1 dose of a COVID-19 vaccine. School districts in both counties implemented variable mask policies at the start of the 2021-22 academic year (Table). The association between school mask policies and school-associated COVID-19 outbreaks in K-12 public noncharter schools open for in-person learning in Maricopa and Pima Counties during July 15-August 31, 2021, was evaluated.


Subject(s)
COVID-19/prevention & control , Disease Outbreaks/statistics & numerical data , Masks/statistics & numerical data , Organizational Policy , Schools/organization & administration , Adolescent , Arizona/epidemiology , COVID-19/epidemiology , Child , Child, Preschool , Humans
14.
J Cardiovasc Med (Hagerstown) ; 23(1): 22-27, 2022 01 01.
Article in English | MEDLINE | ID: covidwho-1430637

ABSTRACT

AIMS: The coronavirus disease-19 (COVID-19) outbreak has been recently associated with lower hospitalization rates for acute coronary syndromes. Aim of the study was to investigate whether a similar behaviour is observed in admissions for urgent pacemaker implant. METHODS: This retrospective study included 1315 patients from 18 hospitals in Northern Italy with a high number of COVID-19 cases. Hospitalization rates for urgent pacemaker implant were compared between the following periods: 20 February to 20 April 2020 (case period); from 1 January to 19 February 2020 (intra-year control period); from 20 February to 20 April 2019 (inter-year control period). RESULTS: The incidence rate of urgent implants was 5.0/day in the case period, 6.0/day in the intra-year control period and 5.8/day in the inter-year control period. Incidence rate in the case period was significantly lower than both the intra-year [incidence rate ratio (IRR): 0.81, 95% CI 0.67-0.99, P = 0.040] and inter-year control periods (IRR: 0.79, 95% CI 0.66-0.95, P = 0.012); this reduction was highest after the national lockdown (IRR 0.68, 95% CI 0.52-0.91, P = 0.009). The prevalence of residents in rural areas undergoing urgent pacemaker implant was lower in the case period (36%) than in both the intra-year (47%, P = 0.03) and inter-year control periods (51%, P = 0.002). Elective pacemaker implants also decreased in the case period, with the incidence rate here being 3.5/day vs. 6.4/day in the intra-year (-45%) and 6.9/day in the inter-year period (-49%). CONCLUSION: Despite severe clinical patterns, the COVID-19 outbreak has negatively affected the population presentation to Emergency Departments for bradyarrhythmias requiring urgent pacemaker implant in Northern Italy. This mainly occurred after the national lockdown and concerned patients living in rural areas.


Subject(s)
Bradycardia/epidemiology , Bradycardia/therapy , COVID-19/epidemiology , Disease Outbreaks/statistics & numerical data , Elective Surgical Procedures/statistics & numerical data , Emergencies/epidemiology , Pacemaker, Artificial/statistics & numerical data , Aged , Aged, 80 and over , Female , Hospitalization/statistics & numerical data , Humans , Incidence , Italy/epidemiology , Male , Retrospective Studies
16.
J Med Virol ; 94(1): 413-416, 2022 01.
Article in English | MEDLINE | ID: covidwho-1404587

ABSTRACT

In December 2020, Italy experienced the first case of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) B.1.1.7 lineage. In January 2021, we identified 21 cases of this variant in Corzano, defining the first outbreak of SARS-CoV-2 B.1.1.7 lineage in Italy. The high transmissibility of the B.1.1.7 variant represented an important benefit for the virus, which became rapidly dominant on the territory. Containment measures induced the epidemic curve onto a decreasing trajectory underlining the importance of appropriate control and surveillance for restraint of virus spread. Highlights The first Italian outbreak of SARS-CoV-2 B.1.1.7 lineage occurred in Lombardy in January 2021. The outbreak originated by a single introduction of the B.1.1.7 lineage. The genomic sequencing revealed, for the first time, the presence of the V551F mutation in the B.1.1.7 lineage in Italy. Surveillance, prompt sequencing and tracing efforts were fundamental to identify and to quickly contain the outbreak.


Subject(s)
COVID-19 Nucleic Acid Testing , COVID-19/epidemiology , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Adolescent , Adult , COVID-19/transmission , Child , Child, Preschool , Disease Outbreaks/statistics & numerical data , Female , Genome, Viral/genetics , High-Throughput Nucleotide Sequencing , Humans , Infection Control/methods , Italy/epidemiology , Male , Middle Aged , Phylogeny , Sequence Analysis, RNA , Whole Genome Sequencing , Young Adult
17.
Sci Rep ; 11(1): 17905, 2021 09 09.
Article in English | MEDLINE | ID: covidwho-1402120

ABSTRACT

COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic has evolved leads to increasing errors in our ability to predict the spread of the disease. This work seeks to explain this diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of both time and the disease's prevalence. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization to overcome the challenge of partial observability, due to the fact that the system's state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it can exhibit both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We then compare the error of simulations from our model with a standard SEIRD model, and show that ours substantially reduces errors. We also use simulated data to compare our methodology for handling partial observability with a standard approach, showing that ours is significantly better at estimating the values of unobserved quantities.


Subject(s)
COVID-19/epidemiology , Computer Simulation , Disease Hotspot , Models, Statistical , Computational Biology , Disease Outbreaks/statistics & numerical data , Humans , Neural Networks, Computer , Prevalence , SARS-CoV-2 , United States/epidemiology
18.
Nat Microbiol ; 6(10): 1271-1278, 2021 10.
Article in English | MEDLINE | ID: covidwho-1402078

ABSTRACT

Genomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook 'Data for Good' and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.


Subject(s)
COVID-19/transmission , Cell Phone/statistics & numerical data , Genome, Viral/genetics , SARS-CoV-2/genetics , Social Media/statistics & numerical data , Bangladesh/epidemiology , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Genomics , Health Policy/legislation & jurisprudence , Humans , Phylogeny , Population Dynamics/statistics & numerical data , SARS-CoV-2/classification , Travel/legislation & jurisprudence , Travel/statistics & numerical data
19.
Public Health Res Pract ; 31(3)2021 Sep 08.
Article in English | MEDLINE | ID: covidwho-1399672

ABSTRACT

OBJECTIVES: To describe local operational aspects of the coronavirus disease 2019 (COVID-19) response during the first three waves of outbreaks in New South Wales (NSW), Australia, which began in January, July and December 2020. Type of program or service: Public health outbreak response. METHODS: Narrative with epidemiological linking and genomic testing. RESULTS: Epidemiological linking and genomic testing found that during the first wave of COVID-19 in NSW, a large number of community transmissions went undetected because of limited testing for the virus and limited contact tracing of cases. The second wave of COVID-19 in NSW emerged following reintroduction from the second wave in Victoria, Australia in July 2020, and the third wave followed undetected introduction from overseas. By the second and third waves, cases could be more effectively detected and isolated through an increased ability to test and contact trace, and to rapidly genomic sequence severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) isolates, allowing most cases to be identified and epidemiologically linked. This greater certainty in understanding chains of transmission resulted in control of the outbreaks despite less stringent restrictions on the community, by using a refined strategy of targeted shutdown, restrictions on cases, their close contacts, identified hotspots and venues of concern rather than a whole of community lockdown. Risk assessments of potential transmission sites were constantly updated through our evolving experience with transmission events. However, this refined strategy did leave the potential for large point source outbreaks should any cases go undetected. [Addendum] A fourth wave that began in Sydney in June 2021 challenged this strategy due to the more transmissible nature of the Delta variant of SARS-CoV-2. LESSONS LEARNT: A wave of COVID-19 infections can develop quickly from one infected person. The community needs to remain vigilant, adhering to physical distancing measures, signing in to venues they visit, and getting tested if they have any symptoms. Signing out of venues on exit allows public health resources to be used more efficiently to respond to outbreaks.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control/methods , Disease Outbreaks/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19 Testing/methods , Child , Child, Preschool , Communicable Disease Control/organization & administration , Contact Tracing/methods , Disease Outbreaks/prevention & control , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , New South Wales/epidemiology , Physical Distancing , Public Health , Quarantine/methods , SARS-CoV-2/isolation & purification , Victoria/epidemiology , Young Adult
20.
PLoS Comput Biol ; 17(9): e1009334, 2021 09.
Article in English | MEDLINE | ID: covidwho-1398921

ABSTRACT

Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible-exposed-infectious-recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC's government's website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.


Subject(s)
COVID-19 , Disease Outbreaks/statistics & numerical data , Models, Statistical , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control , Computational Biology , Humans , New York City/epidemiology , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL
...