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1.
Sci Rep ; 12(1): 5241, 2022 03 28.
Article in English | MEDLINE | ID: mdl-35347208

ABSTRACT

Serosurveillance helps establish reopening guidelines and determine the immunity levels in different populations to reach herd immunity. Then, there is an urgent need to estimate seroprevalence population wide. In Mexico, information about COVID-19 cases and related deaths is scarce. Also, there is no official serosurveillance, limiting our knowledge of the impact of the SARS-CoV-2 pandemic. Here, we report the prevalence of anti-SARS-CoV-2 antibodies in 522,690 unvaccinated people from July 5th to December 31st, 2020. The overall seroprevalence was 32.8% and highest in adults aged 30-39 years (38.5%) than people under 20 years (33.0%) or older (28.9%). Moreover, in a cohort of 1655 individuals confirmed COVID-19 by PCR, we found that symptomatic people (HR = 2.56) increased seroconversion than presymptomatic. Also, we identified that the most discriminative symptoms for COVID-19 that could predict seroconversion were anosmia and ageusia (HR = 1.70), fever, myalgia/arthralgia, and cough (HR = 1.75). Finally, we found that obese people had lower seroconversion (HR = 0.53) than healthy people, but the opposite happens in diabetic people (HR = 1.39). These findings reveal that around one-third of Mexican outpatients had anti-SARS-CoV-2 antibodies before vaccination. Also, some symptoms improve empirically COVID-19 diagnosis and seroconversion. This information could help fine-tune vaccination schemes and the reopening and back-to-work algorithms.


Subject(s)
COVID-19 , SARS-CoV-2 , Adult , COVID-19/epidemiology , COVID-19 Testing , Disease Outbreaks , Humans , Mexico/epidemiology , Seroconversion , Seroepidemiologic Studies
2.
Internet resource in English, Spanish, Portuguese | LIS -Health Information Locator | ID: lis-48877

ABSTRACT

A Organização Pan-Americana da Saúde (OPAS) disponibilizou novas orientações aos laboratórios da região para contribuir com as investigações sobre as causas da hepatite de origem desconhecida em crianças.


Subject(s)
Pan American Health Organization , Hepatitis , Child , Disease Outbreaks
3.
Lima; Perú. Ministerio de Salud; Jun. 2022. 20 p.
Monography in Spanish | MINSAPERÚ, MINSAPERÚ | ID: biblio-1372726

ABSTRACT

La guía contiene la estandarización de los procedimientos para la investigación y control de brotes dela COVID-19 en instituciones con población cautiva en el territorio nacional.


Subject(s)
Population , Reference Standards , Research , Disease Outbreaks , COVID-19
4.
Geneva; World Health Organization; 2022-06-01.
in English | WHO IRIS | ID: who-354776
5.
Geneva; World Health Organization; 2022-06-15.
in English | WHO IRIS | ID: who-356571
6.
Geneva; World Health Organization; 2022-06-08.
in English | WHO IRIS | ID: who-355779
7.
Int J Med Inform ; 164: 104807, 2022 Aug.
Article in English | MEDLINE | ID: mdl-35671585

ABSTRACT

PURPOSE: COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making. We assessed performances of three machine learning approaches to predict mortality in COVID-19 patients admitted to ICU using early operative data from the Lombardy ICU Network. METHODS: This is a secondary analysis of prospectively collected data from Lombardy ICU network. A logistic regression, balanced logistic regression and random forest were built to predict survival on two datasets: dataset A included patient demographics, medications before admission and comorbidities, and dataset B included respiratory data the first day in ICU. RESULTS: Models were trained on 1484 patients on four outcomes (7/14/21/28 days) and reached the greatest predictive performance at 28 days (F1-score: 0.75 and AUC: 0.80). Age, number of comorbidities and male gender were strongly associated with mortality. On dataset B, mode of ventilatory assistance at ICU admission and fraction of inspired oxygen were associated with an increase in prediction performances. CONCLUSIONS: Machine learning techniques might be useful in emergency phases to reach good predictive performances maintaining interpretability to gain knowledge on complex situations and enhance patient management and resources.


Subject(s)
COVID-19 , COVID-19/epidemiology , Critical Illness/epidemiology , Disease Outbreaks , Humans , Intensive Care Units , Male , Retrospective Studies , SARS-CoV-2 , Supervised Machine Learning
8.
Int J Prison Health ; ahead-of-print(ahead-of-print)2022 Jun 10.
Article in English | MEDLINE | ID: mdl-35678718

ABSTRACT

PURPOSE: This study aims to characterize the June 2020 COVID-19 outbreak at San Quentin California State Prison and to describe what made San Quentin so vulnerable to uncontrolled transmission. DESIGN/METHODOLOGY/APPROACH: Since its onset, the COVID-19 pandemic has exposed and exacerbated the profound health harms of carceral settings, such that nearly half of state prisons reported COVID-19 infection rates that were four or more times (and up to 15 times) the rate found in the state's general population. Thus, addressing the public health crises and inequities of carceral settings during a respiratory pandemic requires analyzing the myriad factors shaping them. In this study, we reported observations and findings from environmental risk assessments during visits to San Quentin California State Prison. We complemented our assessments with analyses of administrative data. FINDINGS: For future respiratory pathogens that cannot be prevented with effective vaccines, this study argues that outbreaks will no doubt occur again without robust implementation of additional levels of preparedness - improved ventilation, air filtration, decarceration with emergency evacuation planning - alongside addressing the vulnerabilities of carceral settings themselves. ORIGINALITY/VALUE: This study addresses two critical aspects that are insufficiently covered in the literature: how to prepare processes to safely implement emergency epidemic measures when needed, such as potential evacuation, and how to address unique challenges throughout an evolving pandemic for each carceral setting.


Subject(s)
COVID-19 , Pandemics , COVID-19/epidemiology , California/epidemiology , Disease Outbreaks/prevention & control , Humans , Pandemics/prevention & control , Prisons
9.
MMWR Morb Mortal Wkly Rep ; 71(23): 764-769, 2022 Jun 10.
Article in English | MEDLINE | ID: mdl-35679181

ABSTRACT

On May 17, 2022, the Massachusetts Department of Public Health (MDPH) Laboratory Response Network (LRN) laboratory confirmed the presence of orthopoxvirus DNA via real-time polymerase chain reaction (PCR) from lesion swabs obtained from a Massachusetts resident. Orthopoxviruses include Monkeypox virus, the causative agent of monkeypox. Subsequent real-time PCR testing at CDC on May 18 confirmed that the patient was infected with the West African clade of Monkeypox virus. Since then, confirmed cases* have been reported by nine states. In addition, 28 countries and territories,† none of which has endemic monkeypox, have reported laboratory-confirmed cases. On May 17, CDC, in coordination with state and local jurisdictions, initiated an emergency response to identify, monitor, and investigate additional monkeypox cases in the United States. This response has included releasing a Health Alert Network (HAN) Health Advisory, developing interim public health and clinical recommendations, releasing guidance for LRN testing, hosting clinician and public health partner outreach calls, disseminating health communication messages to the public, developing protocols for use and release of medical countermeasures, and facilitating delivery of vaccine postexposure prophylaxis (PEP) and antivirals that have been stockpiled by the U.S. government for preparedness and response purposes. On May 19, a call center was established to provide guidance to states for the evaluation of possible cases of monkeypox, including recommendations for clinical diagnosis and orthopoxvirus testing. The call center also gathers information about possible cases to identify interjurisdictional linkages. As of May 31, this investigation has identified 17§ cases in the United States; most cases (16) were diagnosed in persons who identify as gay, bisexual, or men who have sex with men (MSM). Ongoing investigation suggests person-to-person community transmission, and CDC urges health departments, clinicians, and the public to remain vigilant, institute appropriate infection prevention and control measures, and notify public health authorities of suspected cases to reduce disease spread. Public health authorities are identifying cases and conducting investigations to determine possible sources and prevent further spread. This activity was reviewed by CDC and conducted consistent with applicable federal law and CDC policy.¶.


Subject(s)
Malaria , Monkeypox , Sexual and Gender Minorities , Animals , Disease Outbreaks , Homosexuality, Male , Humans , Malaria/diagnosis , Male , Monkeypox/diagnosis , Monkeypox/epidemiology , Population Surveillance , Travel , United States/epidemiology
10.
PLoS One ; 17(6): e0269687, 2022.
Article in English | MEDLINE | ID: mdl-35679235

ABSTRACT

The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to other disciplines and as the primary driver of solutions for, e.g., diagnostics, social distancing, and contact tracing. In this work, we surveyed the Italian computer science and engineering community initiatives against the Covid19 pandemic. The 128 responses thus collected document the response of such a community during the first pandemic wave in Italy (February-May 2020), through several initiatives carried out by both single researchers and research groups able to promptly react to Covid19, even remotely. The data obtained by the survey are here reported, discussed and further investigated by Natural Language Processing techniques, to generate semantic clusters based on embedding representations of the surveyed activity descriptions. The resulting clusters have been then used to extend an existing Covid19 taxonomy with the classification of related research activities in computer science and information technology areas, summarizing this work contribution through a reproducible survey-to-taxonomy methodology.


Subject(s)
COVID-19 , COVID-19/epidemiology , Cluster Analysis , Disease Outbreaks , Humans , Italy/epidemiology , Pandemics/prevention & control , Physical Distancing
11.
BMC Med ; 20(1): 202, 2022 Jun 16.
Article in English | MEDLINE | ID: mdl-35705986

ABSTRACT

BACKGROUND: Despite large outbreaks in humans seeming improbable for a number of zoonotic pathogens, several pose a concern due to their epidemiological characteristics and evolutionary potential. To enable effective responses to these pathogens in the event that they undergo future emergence, the Coalition for Epidemic Preparedness Innovations is advancing the development of vaccines for several pathogens prioritized by the World Health Organization. A major challenge in this pursuit is anticipating demand for a vaccine stockpile to support outbreak response. METHODS: We developed a modeling framework for outbreak response for emerging zoonoses under three reactive vaccination strategies to assess sustainable vaccine manufacturing needs, vaccine stockpile requirements, and the potential impact of the outbreak response. This framework incorporates geographically variable zoonotic spillover rates, human-to-human transmission, and the implementation of reactive vaccination campaigns in response to disease outbreaks. As proof of concept, we applied the framework to four priority pathogens: Lassa virus, Nipah virus, MERS coronavirus, and Rift Valley virus. RESULTS: Annual vaccine regimen requirements for a population-wide strategy ranged from > 670,000 (95% prediction interval 0-3,630,000) regimens for Lassa virus to 1,190,000 (95% PrI 0-8,480,000) regimens for Rift Valley fever virus, while the regimens required for ring vaccination or targeting healthcare workers (HCWs) were several orders of magnitude lower (between 1/25 and 1/700) than those required by a population-wide strategy. For each pathogen and vaccination strategy, reactive vaccination typically prevented fewer than 10% of cases, because of their presently low R0 values. Targeting HCWs had a higher per-regimen impact than population-wide vaccination. CONCLUSIONS: Our framework provides a flexible methodology for estimating vaccine stockpile needs and the geographic distribution of demand under a range of outbreak response scenarios. Uncertainties in our model estimates highlight several knowledge gaps that need to be addressed to target vulnerable populations more accurately. These include surveillance gaps that mask the true geographic distribution of each pathogen, details of key routes of spillover from animal reservoirs to humans, and the role of human-to-human transmission outside of healthcare settings. In addition, our estimates are based on the current epidemiology of each pathogen, but pathogen evolution could alter vaccine stockpile requirements.


Subject(s)
Epidemics , Middle East Respiratory Syndrome Coronavirus , Vaccines , Animals , Disease Outbreaks/prevention & control , Epidemics/prevention & control , Humans , Zoonoses/epidemiology , Zoonoses/prevention & control
12.
J Vector Borne Dis ; 59(1): 70-78, 2022.
Article in English | MEDLINE | ID: mdl-35708407

ABSTRACT

BACKGROUND & OBJECTIVES: Kyasanur Forest Disease (KFD) is a vector borne haemorrhagic fever that is endemic in the Wayanad region located in Northern part of Kerala, India. The region is managing the outbreak well ever since the major epidemic of 2015. This was because of the successful implementation of One Health (OH) initiative concentrating on multisectoral collaboration between regional institutions involved in public, animal and environmental health domains. The article presents how OH was implemented for the first time in the district in the year 2015 and evaluates the degree OH-ness of the Initiative. METHODS: The OH approach involved trans-disciplinary stakeholder meetings and reviews, outbreak management and integrated surveillance targeting ticks, monkeys and humans. The degree of OH-ness used for addressing KFD during the year 2015 was evaluated following the protocol developed by the Network for Evaluation of One Health (NEOH). In detail, we (i) described the OH initiative and its system (Aim, stakeholders, action strategy) and (ii) scored different aspects of this initiative (i.e., OH-thinking, -planning, -working, -sharing, -learning, -organization), with values from 0 (=no OH approach) to 1 (=perfect OH approach). RESULTS: We obtained a median score for each aspect evaluated. We reached high scores for OH systemic organization (1.0), OH thinking (0.83) and OH working (0.83). Lower scores were attributed to OH planning (0.58), OH sharing (0.50) and OH learning (0.33). The OH index was 0.36 and OH ratio was 0.95, indicating a balance between the OH operations and supporting infrastructures. INTERPRETATION & CONCLUSION: With this we could high-light some critical issues related to communication on sharing data as well as learning gaps for consideration to control future outbreaks. The strengths and weaknesses detected may be used to refine the initiative, aiming to provide a basis for the development of shared recommendations in a more OH-oriented perspective. This model of evaluation criteria will serve to create a database of OH success stories in India that will in turn help to institutionalize the approach at ministerial level. Future India is moving towards implementing a One Health, hence, this study data will provide an ideal opportunity for all sectors to control any vector borne diseases.


Subject(s)
Kyasanur Forest Disease , One Health , Animals , Disease Outbreaks/prevention & control , Disease Vectors , India/epidemiology , Kyasanur Forest Disease/epidemiology , Kyasanur Forest Disease/prevention & control
13.
J Vector Borne Dis ; 59(1): 79-85, 2022.
Article in English | MEDLINE | ID: mdl-35708408

ABSTRACT

BACKGROUND & OBJECTIVES: In India, Kyasanur Forest Disease has been reported from the states of Karnataka, Kerala, Goa, and Maharashtra. The relationship between climatic factors and transmission of KFD remains untouched, therefore, the present study was undertaken. METHODS: Based on the occurrence of cases, Shivamogga district (Karnataka) and Wayanad district in Kerala and northern Goa (Goa state) were selected for the study. Data on the incidence of KFD and climate factors were collected from concerned authorities. To determine the relationship between dependent and independent variables, spearman's correlation was calculated for monthly as well as with lag months. RESULTS: KFD cases and temperature (°C) were found significantly correlated up to 1 months' lag period (p<0.05) while with precipitation relationship was found negatively significant for 0-3 months' lag. The range of suitable temperature for KFD in Shivamogga, Goa and Wayanad was found as 20-31°C, 25-29°C and 27-31°C respectively. The cumulative precipitation during transmission months (November-May) ranged from <150-500mm, while in non-transmission months (June-October) from >1100-2400mm. INTERPRETATION & CONCLUSION: The analysis of three sites revealed that with the increase in temperature, the intensity of KFD transmission decreases as corroborated by the seasonal fluctuations in Shivamogga, Goa and Wayanad. High precipitation from June to October rovides suitable ecology to tick vector and sets in transmission season from November to May when cumulative precipitation is <500 mm.


Subject(s)
Kyasanur Forest Disease , Ticks , Animals , Disease Outbreaks , Incidence , India/epidemiology , Kyasanur Forest Disease/epidemiology
15.
Emerg Infect Dis ; 28(7): 1525-1527, 2022 07.
Article in English | MEDLINE | ID: mdl-35642471

ABSTRACT

We report enterovirus D68 circulation in Maryland, USA, during September-October 2021, which was associated with a spike in influenza-like illness. The characterized enterovirus D68 genomes clustered within the B3 subclade that circulated in 2018 in Europe and the United States.


Subject(s)
Enterovirus D, Human , Enterovirus Infections , Enterovirus , Influenza, Human , Respiratory Tract Infections , Virus Diseases , Disease Outbreaks , Enterovirus D, Human/genetics , Humans , Influenza, Human/complications , Influenza, Human/epidemiology , Maryland/epidemiology , Phylogeny , Respiratory Tract Infections/epidemiology , United States/epidemiology
16.
Emerg Infect Dis ; 28(7): 1446-1450, 2022 07.
Article in English | MEDLINE | ID: mdl-35642480

ABSTRACT

Avian influenza A(H5N8) virus has caused major epizootics in Europe since 2016. We conducted virologic analysis of aerosol and dust collected on poultry farms in France during 2020-2021. Our results suggest dust contributes to viral dispersal, even early in an outbreak, and could be a valuable surveillance tool.


Subject(s)
Influenza A Virus, H5N8 Subtype , Influenza in Birds , Influenza, Human , Poultry Diseases , Animals , Animals, Wild , Birds , Disease Outbreaks/veterinary , Dust , Farms , France/epidemiology , Humans , Influenza A Virus, H5N8 Subtype/genetics , Influenza in Birds/epidemiology , Influenza, Human/epidemiology , Phylogeny , Poultry , Poultry Diseases/epidemiology
17.
Article in English | MEDLINE | ID: mdl-35682349

ABSTRACT

Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating social and economic ripple effects, and the importance of PHSMs (Public Health and Social Measures) is being highlighted to cope with this severe situation. Accordingly, there has also been an increase in research related to a decision support system based on simulation approaches used as a basis for PHSMs. However, previous studies showed limitations impeding utilization as a decision support system for policy establishment and implementation, such as the failure to reflect changes in the effectiveness of PHSMs and the restriction to short-term forecasts. Therefore, this study proposes an LSTM-Autoencoder-based decision support system for establishing and implementing PHSMs. To overcome the limitations of existing studies, the proposed decision support system used a methodology for predicting the number of daily confirmed cases over multiple periods based on multiple output strategies and a methodology for rapidly identifying varies in policy effects based on anomaly detection. It was confirmed that the proposed decision support system demonstrated excellent performance compared to models used for time series analysis such as statistical models and deep learning models. In addition, we endeavored to increase the usability of the proposed decision support system by suggesting a transfer learning-based methodology that can efficiently reflect variations in policy effects. Finally, the decision support system proposed in this study provides a methodology that provides multi-period forecasts, identifying variations in policy effects, and efficiently reflects the effects of variation policies. It was intended to provide reasonable and realistic information for the establishment and implementation of PHSMs and, through this, to yield information expected to be highly useful, which had not been provided in the decision support systems presented in previous studies.


Subject(s)
COVID-19 , Deep Learning , COVID-19/epidemiology , Disease Outbreaks , Humans , Pandemics/prevention & control
18.
J Hazard Mater ; 430: 128504, 2022 05 15.
Article in English | MEDLINE | ID: mdl-35739650

ABSTRACT

Airborne transmission of SARS-CoV-2 has been increasingly recognized in the outbreak of COVID-19, especially with the Omicron variant. We investigated an outbreak due to Omicron variant in a restaurant. Besides epidemiological and phylogenetic analyses, the secondary attack rates of customers of restaurant-related COVID-19 outbreak before (Outbreak R1) and after enhancement of indoor air dilution (Outbreak R2) were compared. On 27th December 2021, an index case stayed in restaurant R2 for 98 min. Except for 1 sitting in the same table, six other secondary cases sat in 3 corners at 3 different zones, which were served by different staff. The median exposure time was 34 min (range: 19-98 min). All 7 secondary cases were phylogenetically related to the index. Smoke test demonstrated that the airflow direction may explain the distribution of secondary cases. Compared with an earlier COVID-19 outbreak in another restaurant R1 (19th February 2021), which occurred prior to the mandatory enhancement of indoor air dilution, the secondary attack rate among customers in R2 was significantly lower than that in R1 (3.4%, 7/207 vs 28.9%, 22/76, p<0.001). Enhancement of indoor air dilution through ventilation and installation of air purifier could minimize the risk of SARS-CoV-2 transmission in the restaurants.


Subject(s)
Air Pollution, Indoor , COVID-19 , COVID-19/epidemiology , Disease Outbreaks , Humans , Phylogeny , Restaurants , SARS-CoV-2/genetics
19.
Nat Commun ; 13(1): 3618, 2022 Jun 24.
Article in English | MEDLINE | ID: mdl-35750868

ABSTRACT

Monitoring population protective immunity against SARS-CoV-2 variants is critical for risk assessment. We hypothesize that Hong Kong's explosive Omicron BA.2 outbreak in early 2022 could be explained by low herd immunity. Our seroprevalence study using sera collected from January to December 2021 shows a very low prevalence of neutralizing antibodies (NAb) against ancestral virus among older adults. The age group-specific prevalence of NAb generally correlates with the vaccination uptake rate, but older adults have a much lower NAb seropositive rate than vaccination uptake rate. For all age groups, the seroprevalence of NAb against Omicron variant is much lower than that against the ancestral virus. Our study suggests that this BA.2 outbreak and the exceptionally high case-fatality rate in the ≥80 year-old age group (9.2%) could be attributed to the lack of protective immunity in the population, especially among the vulnerable older adults, and that ongoing sero-surveillance is essential.


Subject(s)
COVID-19 , SARS-CoV-2 , Aged , Aged, 80 and over , Antibodies, Neutralizing , Antibodies, Viral , COVID-19/epidemiology , Disease Outbreaks , Hong Kong/epidemiology , Humans , Seroepidemiologic Studies
20.
N Z Med J ; 135: 66-76, 2022 Apr 01.
Article in English | MEDLINE | ID: mdl-35728185

ABSTRACT

AIM: The August 2021 COVID-19 outbreak in Auckland caused the New Zealand Government to transition from an elimination strategy to suppression, which relies heavily on high vaccination rates in the population. As restrictions ease and as COVID-19 spreads throughout New Zealand, there is a need to understand how different levels of vaccination will impact the initial stages of COVID-19 outbreaks that are seeded around the country. METHOD: A stochastic branching process model is used to simulate the initial spread of a COVID-19 outbreak for different vaccination rates. RESULTS: High vaccination rates are effective at minimising the number of infections and hospitalisations. Increasing vaccination rates from 20% (approximate value at the start of the August 2021 outbreak) to 80% (approximate proposed target) of the total population can reduce the median number of infections that occur within the first four weeks of an outbreak from 1011 to 14 (25th and 75th quantiles of 545-1602 and 2-32 for V=20% and V=80%, respectively). As the vaccination rate increases, the number of breakthrough infections (infections in fully vaccinated individuals) and hospitalisations of vaccinated individuals increases. Unvaccinated individuals, however, are 3.3x more likely to be infected with COVID-19 and 25x more likely to be hospitalised. CONCLUSION: This work demonstrates the importance of vaccination in protecting individuals from COVID-19, preventing high caseloads, and minimising the number of hospitalisations and hence limiting the pressure on the healthcare system.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Disease Outbreaks/prevention & control , Humans , New Zealand/epidemiology , Vaccination
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