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1.
JMIR Public Health Surveill ; 7(3): e27317, 2021 03 29.
Article in English | MEDLINE | ID: covidwho-2197905

ABSTRACT

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.


Subject(s)
Air Travel/statistics & numerical data , COVID-19 , Global Health , Public Health , Spatio-Temporal Analysis , Coronavirus Infections/epidemiology , Disease Outbreaks/statistics & numerical data , Humans
2.
JMIR Public Health Surveill ; 7(6): e24251, 2021 06 17.
Article in English | MEDLINE | ID: covidwho-2197876

ABSTRACT

BACKGROUND: COVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia. OBJECTIVE: This study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks. METHODS: We extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Traditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India's speed of new infections per 100,000 population increased by 52% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak. CONCLUSIONS: Relaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/statistics & numerical data , Disease Outbreaks/statistics & numerical data , Health Policy , Public Health/statistics & numerical data , Adult , Aged , Aged, 80 and over , Asia/epidemiology , COVID-19/prevention & control , Communicable Disease Control/legislation & jurisprudence , Female , Humans , Longitudinal Studies , Male , Middle Aged , Public Health Surveillance , SARS-CoV-2
3.
Hypertension ; 76(5): 1526-1536, 2020 11.
Article in English | MEDLINE | ID: covidwho-2153220

ABSTRACT

ACE2 (angiotensin-converting enzyme 2) is a key component of the renin-angiotensin-aldosterone system. Yet, little is known about the clinical and biologic correlates of circulating ACE2 levels in humans. We assessed the clinical and proteomic correlates of plasma (soluble) ACE2 protein levels in human heart failure. We measured plasma ACE2 using a modified aptamer assay among PHFS (Penn Heart Failure Study) participants (n=2248). We performed an association study of ACE2 against ≈5000 other plasma proteins measured with the SomaScan platform. Plasma ACE2 was not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 was associated with older age, male sex, diabetes mellitus, a lower estimated glomerular filtration rate, worse New York Heart Association class, a history of coronary artery bypass surgery, and higher pro-BNP (pro-B-type natriuretic peptide) levels. Plasma ACE2 exhibited associations with 1011 other plasma proteins. In pathway overrepresentation analyses, top canonical pathways associated with plasma ACE2 included clathrin-mediated endocytosis signaling, actin cytoskeleton signaling, mechanisms of viral exit from host cells, EIF2 (eukaryotic initiation factor 2) signaling, and the protein ubiquitination pathway. In conclusion, in humans with heart failure, plasma ACE2 is associated with various clinical factors known to be associated with severe coronavirus disease 2019 (COVID-19), including older age, male sex, and diabetes mellitus, but is not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 protein levels are prominently associated with multiple cellular pathways involved in cellular endocytosis, exocytosis, and intracellular protein trafficking. Whether these have a causal relationship with ACE2 or are relevant to novel coronavirus-2 infection remains to be assessed in future studies.


Subject(s)
Coronavirus Infections/epidemiology , Disease Outbreaks/statistics & numerical data , Disease Progression , Heart Failure/enzymology , Heart Failure/physiopathology , Peptidyl-Dipeptidase A/blood , Pneumonia, Viral/epidemiology , Academic Medical Centers , Analysis of Variance , Angiotensin-Converting Enzyme 2 , Biomarkers/metabolism , COVID-19 , Cohort Studies , Coronavirus Infections/prevention & control , Female , Humans , Linear Models , Male , Middle Aged , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Prognosis , Proportional Hazards Models , Proteomics/methods , Retrospective Studies , Sensitivity and Specificity , Severity of Illness Index , United States
7.
Epidemiol Infect ; 149: e261, 2021 05 14.
Article in English | MEDLINE | ID: covidwho-1647899

ABSTRACT

Epidemic intelligence activities are undertaken by the WHO Regional Office for Africa to support member states in early detection and response to outbreaks to prevent the international spread of diseases. We reviewed epidemic intelligence activities conducted by the organisation from 2017 to 2020, processes used, key results and how lessons learned can be used to strengthen preparedness, early detection and rapid response to outbreaks that may constitute a public health event of international concern. A total of 415 outbreaks were detected and notified to WHO, using both indicator-based and event-based surveillance. Media monitoring contributed to the initial detection of a quarter of all events reported. The most frequent outbreaks detected were vaccine-preventable diseases, followed by food-and-water-borne diseases, vector-borne diseases and viral haemorrhagic fevers. Rapid risk assessments generated evidence and provided the basis for WHO to trigger operational processes to provide rapid support to member states to respond to outbreaks with a potential for international spread. This is crucial in assisting member states in their obligations under the International Health Regulations (IHR) (2005). Member states in the region require scaled-up support, particularly in preventing recurrent outbreaks of infectious diseases and enhancing their event-based surveillance capacities with automated tools and processes.


Subject(s)
Epidemics/prevention & control , Public Health Surveillance/methods , World Health Organization/organization & administration , Africa/epidemiology , Communicable Disease Control , Communicable Diseases/epidemiology , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Global Health , Humans , Risk Assessment
12.
Prev Chronic Dis ; 19: E35, 2022 06 30.
Article in English | MEDLINE | ID: covidwho-1912044

ABSTRACT

INTRODUCTION: Public-facing maps of COVID-19 cases, hospital admissions, and deaths are commonly displayed at the state, county, and zip code levels, and low case counts are suppressed to protect confidentiality. Public health authorities are tasked with case identification, contact tracing, and canvasing for educational purposes during a pandemic. Given limited resources, authorities would benefit from the ability to tailor their efforts to a particular neighborhood or congregate living facility. METHODS: We describe the methods of building a real-time visualization of patients with COVID-19-positive tests, which facilitates timely public health response to the pandemic. We developed an interactive street-level visualization that shows new cases developing over time and resolving after 14 days of infection. Our source data included patient demographics (ie, age, race and ethnicity, and sex), street address of residence, respiratory test results, and date of test. RESULTS: We used colored dots to represent infections. The resulting animation shows where new cases developed in the region and how patterns changed over the course of the pandemic. Users can enlarge specific areas of the map and see street-level detail on residential location of each case and can select from demographic overlays and contour mapping options to see high-level patterns and associations with demographics and chronic disease prevalence as they emerge. CONCLUSIONS: Before the development of this tool, local public health departments in our region did not have a means to map cases of disease to the street level and gain real-time insights into the underlying population where hotspots had developed. For privacy reasons, this tool is password-protected and not available to the public. We expect this tool to prove useful to public health departments as they navigate not only COVID-19 pandemic outcomes but also other public health threats, including chronic diseases and communicable disease outbreaks.


Subject(s)
COVID-19/epidemiology , Pandemics , Public Health/methods , Chronic Disease/epidemiology , Contact Tracing/methods , Demography/methods , Disease Outbreaks/statistics & numerical data , Hospitalization , Humans , Public Health/statistics & numerical data
13.
Sci Rep ; 12(1): 699, 2022 01 13.
Article in English | MEDLINE | ID: covidwho-1900543

ABSTRACT

The global spread of the COVID-19 pandemic has followed complex pathways, largely attributed to the high virus infectivity, human travel patterns, and the implementation of multiple mitigation measures. The resulting geographic patterns describe the evolution of the epidemic and can indicate areas that are at risk of an outbreak. Here, we analyze the spatial correlations of new active cases in the USA at the county level and characterize the extent of these correlations at different times. We show that the epidemic did not progress uniformly and we identify various stages which are distinguished by significant differences in the correlation length. Our results indicate that the correlation length may be large even during periods when the number of cases declines. We find that correlations between urban centers were much more significant than between rural areas and this finding indicates that long-range spreading was mainly facilitated by travel between cities, especially at the first months of the epidemic. We also show the existence of a percolation transition in November 2020, when the largest part of the country was connected to a spanning cluster, and a smaller-scale transition in January 2021, with both times corresponding to the peak of the epidemic in the country.


Subject(s)
COVID-19/transmission , Cities/statistics & numerical data , Disease Outbreaks/statistics & numerical data , Geography/statistics & numerical data , Humans , Pandemics/statistics & numerical data , SARS-CoV-2/pathogenicity , Travel/statistics & numerical data , United States
17.
Epidemiol Infect ; 150: e18, 2021 09 15.
Article in English | MEDLINE | ID: covidwho-1665657

ABSTRACT

Nosocomial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks among health care workers have been scarcely reported so far. This report presents the results of an epidemiologic and molecular investigation of a SARS-CoV-2 outbreak among laundromat facility workers in a large tertiary centre in Israel. Following the first three reported cases of SARS-CoV-2 among laundromat workers, all 49 laundromat personnel were screened by qRT-PCR tests using naso- and oropharingeal swabs. Epidemiologic investigations included questionnaires, interviews and observations of the laundromat facility. Eleven viral RNA samples were then sequenced, and a phylogenetic analysis was performed using MEGAX.The integrated investigation defined three genetic clusters and helped identify the index cases and the assumed routes of transmission. It was then deduced that shared commute and public showers played a role in SARS-CoV-2 transmission in this outbreak, in addition to improper PPE use and social gatherings (such as social eating and drinking). In this study, we present an integrated epidemiologic and molecular investigation may help detect the routes of SARS-CoV-2 transmission, emphasising such routes that are less frequently discussed. Our work reinforces the notion that person-to-person transmission is more likely to cause infections than environmental contamination (e.g. from handling dirty laundry).


Subject(s)
COVID-19/epidemiology , Disease Outbreaks , Laundry Service, Hospital , SARS-CoV-2 , Adult , COVID-19/transmission , Cohort Studies , Contact Tracing , Cross Infection/epidemiology , Cross Infection/transmission , Cross Infection/virology , Disease Outbreaks/statistics & numerical data , Female , Humans , Israel/epidemiology , Male , Middle Aged , Phylogeny , RNA, Viral/chemistry , RNA, Viral/isolation & purification , SARS-CoV-2/classification , SARS-CoV-2/genetics
18.
J Korean Med Sci ; 37(4): e28, 2022 Jan 24.
Article in English | MEDLINE | ID: covidwho-1650103

ABSTRACT

BACKGROUND: A rapid decline in immunity and low neutralizing activity against the delta variant in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccinees has been observed. This study describes an outbreak of coronavirus disease 2019 (COVID-19) breakthrough infections caused by the SARS-CoV-2 delta variant in a psychiatric closed ward. METHODS: Data from epidemic intelligence service officers were utilized to obtain information regarding demographic, vaccination history, and clinical data along with SARS-CoV-2 PCR test results for a COVID-19 outbreak that occurred in a closed psychiatric ward. RESULTS: Among the 164 residents, 144 (87.8%) received two doses of vaccines and 137 (95.1%) of them received ChAdOx1 nCoV-19 vaccine. The mean interval between the second vaccination and COVID-19 diagnosis was 132.77 ± 40.68 days. At the time of detection of the index case, SARS-CoV-2 had spread throughout the ward, infecting 162 of 164 residents. The case-fatality ratio was lower than that in the previously reported outbreak before the vaccination (1.2%, 2/162 vs. 6.9%, P = 0.030). Prolonged hospitalization occurred in 17 patients (11.1%) and was less prevalent in the vaccinated group than in the unvaccinated group (8.5% vs. 25.0%, P = 0.040). CONCLUSION: The findings of this study highlight that while vaccination can reduce mortality and the duration of hospitalization, it is not sufficient to prevent an outbreak of the SARS-CoV-2 delta variant in the present psychiatric hospital setting.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Disease Outbreaks/statistics & numerical data , Psychiatric Department, Hospital/statistics & numerical data , SARS-CoV-2/immunology , COVID-19 Testing , COVID-19 Vaccines , Health Personnel/statistics & numerical data , Hospitalization/statistics & numerical data , Humans , Vaccination/statistics & numerical data
20.
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
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