Impact of COVID-19 on foodborne illness in Africa - A perspective piece.

Food safety investments in Africa, through international donors or national programs, were primarily focused on the formal market sector. However, increasing consumer food safety concerns about foods sold in the growing informal food markets, the rising foodborne disease burden in Africa, and the emergence of COVID-19 have all made food safety a major concern and ultimately brought it to an inflection point in Africa. In addition, Data on foodborne disease outbreaks revealed a scarcity of reported cases before and during the pandemic. The lack of information on foodborne disease reporting in Africa translates into one of the reasons why food safety in Africa is becoming a rising subject matter. This perspective discusses the situation of food safety in Africa before and after the COVID-19 pandemic. Finally, challenges confronting ongoing efforts to improve food safety in the post-COVID era in Africa are summarized and highlighted.

Heterogeneous effects of COVID-19 control measures on tuberculosis in South Korea: an analysis of case notification data.

Coronavirus disease (COVID-19) responses such as social distancing practices can decrease health care access and tuberculosis (TB) notification, particularly among individuals aged 60 years or older. Conversely, they can increase TB notification among younger individuals. These results may be attributable to household transmission and the similarity of TB respiratory symptoms to COVID-19.

Using the Newcomb-Benford law to study the association between a country's COVID-19 reporting accuracy and its development.

The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb-Benford law (NBL) to gauge data accuracy. We run an OLS regression of an index constructed from developmental indicators (democracy level, gross domestic product per capita, healthcare expenditures, and universal healthcare coverage) on goodness-of-fit measures to the NBL. We find that countries with higher values of the developmental index are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests and for a sub-sample of countries with regional data. The NBL provides a first screening for potential data manipulation during pandemics. Our study indicates that data from autocratic regimes and less developed countries should be treated with more caution. The paper further highlights the importance of independent surveillance data verification projects.

Changes in notifiable infectious disease incidence in China during the COVID-19 pandemic.

Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent.

Automated Digital Notification of COVID-19 Diagnoses Through Text and Email Messaging - North Carolina, December 2020-January 2021.

During October 3, 2020-January 9, 2021, North Carolina experienced a 400% increase in daily reported COVID-19 cases (1). To handle the increased number of cases and rapidly notify persons receiving a positive SARS-CoV-2 test result (patients), North Carolina state and local health departments moved from telephone call notification only to telephone call plus automated text and email notification (digital notification) beginning on December 24, 2020. Overall, among 200,258 patients, 142,975 (71%) were notified by telephone call or digital notification within the actionable period (10 days from their diagnosis date)* during January 2021, including at least 112,543 (56%) notified within 24 hours of report to North Carolina state and local health departments, a significantly higher proportion than the 25,905 of 175,979 (15%) notified within 24 hours during the preceding month (p<0.001). Differences in text notification by age, race, and ethnicity were observed. Automated digital notification is a feasible, rapid and efficient method to support timely outreach to patients, provide guidance on how to isolate, access resources, inform close contacts, and increase the efficiency of case investigation staff members.

A Data-Driven Simulation of the Exposure Notification Cascade for Digital Contact Tracing of SARS-CoV-2 in Zurich, Switzerland.

Importance: Digital contact tracing (DCT) apps have been released in several countries to help interrupt SARS-CoV-2 transmission chains. However, the effect of DCT on pandemic mitigation remains to be demonstrated. Objective: To estimate key populations and performance indicators along the exposure notification cascade of the SwissCovid DCT app in a clearly defined regional and temporal context. Design, Setting, and Participants: This comparative effectiveness study was based on a simulation informed by measured data from issued quarantine recommendations and positive SARS-CoV-2 test results after DCT exposure notifications in the canton of Zurich. A stochastic model was developed to re-create the DCT notification cascade for Zurich. Population sizes at each cascade step were estimated using triangulation based on publicly available administrative and observational research data for the study duration from September 1 to October 31, 2020. The resultant estimates were checked for internal consistency and consistency with upstream or downstream estimates in the cascade. Stochastic sampling from data-informed parameter distributions was performed to explore the robustness of results. Subsequently, key performance indicators were evaluated to assess the potential contribution of DCT compared with manual contact tracing. Main Outcomes and Measures: Receiving a voluntary quarantine recommendation and/or a positive SARS-CoV-2 test result after exposure notification. Results: In September 2020, 537 app users received a positive SARS-CoV-2 test result in Zurich, 324 of whom received and entered an upload authorization code. This code triggered an app notification for an estimated 1374 (95% simulation interval [SI], 932-2586) proximity contacts and led to 722 information hotline calls, with an estimated 170 callers (95% SI, 154-186) receiving a quarantine recommendation. An estimated 939 (95% SI, 720-1127) notified app users underwent testing for SARS-CoV-2, of whom 30 (95% SI, 23-36) had positive results after an app notification. Key indicator evaluations revealed that the DCT app triggered quarantine recommendations for the equivalent of 5% of all exposed contacts placed in quarantine by manual contact tracing. For every 10.9 (95% SI, 7.6-15.6) upload authorization codes entered in the app, 1 contact had positive test results for SARS-CoV-2 after app notification. Longitudinal indicator analyses demonstrated bottlenecks in the notification cascade, because capacity limits were reached owing to an increased incidence of SARS-CoV-2 infection in October 2020. Conclusions and Relevance: In this simulation study of the notification cascade of the SwissCovid DCT app, receipt of exposure notifications was associated with quarantine recommendations and identification of SARS-CoV-2-positive cases. These findings in notified proximity contacts reflect important intermediary steps toward transmission prevention.

Early centralized isolation strategy for all confirmed cases of COVID-19 remains a core intervention to disrupt the pandemic spreading significantly.

BACKGROUND: In response to the spread of the coronavirus disease 2019 (COVID-19), plenty of control measures were proposed. To assess the impact of current control measures on the number of new case indices 14 countries with the highest confirmed cases, highest mortality rate, and having a close relationship with the outbreak's origin; were selected and analyzed. METHODS: In the study, we analyzed the impact of five control measures, including centralized isolation of all confirmed cases, closure of schools, closure of public areas, closure of cities, and closure of borders of the 14 targeted countries according to their timing; by comparing its absolute effect average, its absolute effect cumulative, and its relative effect average. RESULTS: Our analysis determined that early centralized isolation of all confirmed cases was represented as a core intervention in significantly disrupting the pandemic's spread. This strategy helped in successfully controlling the early stage of the outbreak when the total number of cases were under 100, without the requirement of the closure of cities and public areas, which would impose a negative impact on the society and its economy. However, when the number of cases increased with the apparition of new clusters, coordination between centralized isolation and non-pharmaceutical interventions facilitated control of the crisis efficiently. CONCLUSION: Early centralized isolation of all confirmed cases should be implemented at the time of the first detected infectious case.

COVID-19 Vaccine Breakthrough Infections Reported to CDC - United States, January 1-April 30, 2021.

COVID-19 vaccines are a critical tool for controlling the ongoing global pandemic. The Food and Drug Administration (FDA) has issued Emergency Use Authorizations for three COVID-19 vaccines for use in the United States.* In large, randomized-controlled trials, each vaccine was found to be safe and efficacious in preventing symptomatic, laboratory-confirmed COVID-19 (1-3). Despite the high level of vaccine efficacy, a small percentage of fully vaccinated persons (i.e. received all recommended doses of an FDA-authorized COVID-19 vaccine) will develop symptomatic or asymptomatic infections with SARS-CoV-2, the virus that causes COVID-19 (2-8).

Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide.

To fight COVID-19, global access to reliable data is vital. Given the rapid acceleration of new cases and the common sense of global urgency, COVID-19 is subject to thorough measurement on a country-by-country basis. The world is witnessing an increasing demand for reliable data and impactful information on the novel disease. Can we trust the data on the COVID-19 spread worldwide? This study aims to assess the reliability of COVID-19 global data as disclosed by local authorities in 202 countries. It is commonly accepted that the frequency distribution of leading digits of COVID-19 data shall comply with Benford's law. In this context, the author collected and statistically assessed 106,274 records of daily infections, deaths, and tests around the world. The analysis of worldwide data suggests good agreement between theory and reported incidents. Approximately 69% of countries worldwide show some deviations from Benford's law. The author found that records of daily infections, deaths, and tests from 28% of countries adhered well to the anticipated frequency of first digits. By contrast, six countries disclosed pandemic data that do not comply with the first-digit law. With over 82 million citizens, Germany publishes the most reliable records on the COVID-19 spread. In contrast, the Islamic Republic of Iran provides by far the most non-compliant data. The author concludes that inconsistencies with Benford's law might be a strong indicator of artificially fabricated data on the spread of SARS-CoV-2 by local authorities. Partially consistent with prior research, the United States, Germany, France, Australia, Japan, and China reveal data that satisfies Benford's law. Unification of reporting procedures and policies globally could improve the quality of data and thus the fight against the deadly virus.

COVID-19 among immigrants in Norway, notified infections, related hospitalizations and associated mortality: A register-based study.

Aim: Research concerning COVID-19 among immigrants is limited. We present epidemiological data for all notified cases of COVID-19 among the 17 largest immigrant groups in Norway, and related hospitalizations and mortality. Methods: We used data on all notified COVID-19 cases in Norway up to 18 October 2020, and associated hospitalizations and mortality, from the emergency preparedness register (including Norwegian Surveillance System for Communicable Diseases) set up by The Norwegian Institute of Public Health to handle the pandemic. We report numbers and rates per 100,000 people for notified COVID-19 cases, and related hospitalizations and mortality in the 17 largest immigrant groups in Norway, crude and with age adjustment. Results: The notification, hospitalization and mortality rates per 100,000 were 251, 21 and five, respectively, for non-immigrants; 567, 62 and four among immigrants; 408, 27 and two, respectively, for immigrants from Europe, North-America and Oceania; and 773, 106 and six, respectively for immigrants from Africa, Asia and South America. The notification rate was highest among immigrants from Somalia (2057), Pakistan (1868) and Iraq (1616). Differences between immigrants and non-immigrants increased when adjusting for age, especially for mortality. Immigrants had a high number of hospitalizations relative to notified cases compared to non-immigrants. Although the overall COVID-19 notification rate was higher in Oslo than outside of Oslo, the notification rate among immigrants compared to non-immigrants was not higher in Oslo than outside. Conclusions: We observed a higher COVID-19 notification rate in immigrants compared to non-immigrants and much higher hospitalization rate, with major differences between different immigrant groups. Somali-, Pakistani- and Iraqi-born immigrants had especially high rates.

Epidemiological analysis of 3,219 COVID-19 outbreaks in the state of Baden-Wuerttemberg, Germany.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emerged as an unprecedented global crisis challenging health systems. This paper aims to assess and characterise SARS-CoV-2 outbreaks in the state of Baden-Wuerttemberg to identify groups at greatest risk, to establish early measures to curb transmission. We analysed all mandatory notified (i.e. laboratory-confirmed) coronavirus disease (COVID-19) outbreaks with more than two cases in Baden-Wuerttemberg from calendar weeks 18-49 (from 27 April to 6 December 2020). We used the following classification for settings: asylum and refugee accommodation, care homes, care facilities, day care child centres, hobby-related, hospitality, hospitals, households, other, residence halls, schools, supported housing, training schools, transportation, treatment facilities and workplace (occupational). We used R program version 3.6.3 for analysis. In our analysis, 3219 outbreaks with 22 238 individuals were included. About 48% were in household and hobby-related settings. Care homes accounted for 9.5% of outbreaks and 21.6% of cases. The median age across all settings was 43 (interquartile range (IQR) 24-63). The median age of cases in care homes was 81 (IQR 56-88). Of all reported cases in care homes, 72.1% were women. Over 30% (466/1511) of hospitalisations were among cases in care homes compared to 17.7% (268/1511) in households. Overall, 70% (500/715) of all deceased persons in outbreaks in the study period were in care homes compared to 4.2% in household settings (30/715). We observed an exponential increase in the number of notified outbreaks starting around the 41st week with N = 291 outbreaks reported in week 49. The median number of cases in outbreaks in care homes and care facilities after the 40th week was 14 (IQR 5-29) and 11 (IQR 5-20), respectively, compared to 3 (IQR 3-5) in households. We observed an increase in hospitalisations, and mortality associated with COVID-19 outbreaks in care homes after the 40th week. We found the care home demographic to be at greatest risk after the 40th week, based on the exponential increase in outbreaks, the number of cases, hospitalisations and mortality trends. Our analysis highlights the necessity of targeted, setting-specific approaches to control transmission in this vulnerable population. Regular screening of staff members and visitors' using rapid antigen point-of-care-tests could be a game-changer in curbing transmission in this setting.

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model.

BACKGROUND: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. METHODS: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. RESULTS: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T212. INTERPRETATION: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case.

The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process's innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.

Officially Confirmed COVID-19 and Unreported COVID-19-Like Illness Death Counts: An Assessment of Reporting Discrepancy in Bangladesh.

Reporting discrepancies between officially confirmed COVID-19 death counts and unreported COVID-19-like illness (CLI) death counts have been evident across the world, including Bangladesh. Publicly available data were used to explore the differences between confirmed COVID-19 death counts and deaths with possible COVID-19 symptoms between March 2, 2020 and August 22, 2020. Unreported CLI death counts totaled more than half of the confirmed COVID-19 death counts during the study period. However, the reporting authority did not consider CLI deaths, which might produce incomplete and unreliable COVID-19 data and respective mortality rates. All deaths with possible COVID-19 symptoms need to be included in provisional death counts to better estimate the COVID-19 mortality rate and to develop data-driven COVID-19 response strategies. An urgent initiative is needed to prepare a comprehensive guideline for reporting COVID-19 deaths.

Analysis of COVID-19 under-reporting in Brazil. / Análise da subnotificação de COVID-19 no Brasil.

OBJECTIVE: To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states. METHODS: We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil's expected case-fatality ratio was also adjusted by the population's age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths). RESULTS: The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%). CONCLUSION: The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.

Where has all the influenza gone? The impact of COVID-19 on the circulation of influenza and other respiratory viruses, Australia, March to September 2020.

The coronavirus disease pandemic was declared in March 2020, as the southern hemisphere's winter approached. Australia expected co-circulation of severe acute respiratory syndrome coronavirus 2, influenza and other seasonal respiratory viruses. However, influenza notifications were 7,029 (March-September) compared with an average 149,832 for the same period in 2015-2019 [corrected], despite substantial testing. Restrictions on movement within and into Australia may have temporarily eliminated influenza. Other respiratory pathogens also showed remarkably changed activity in 2020.

Virological surveillance of influenza viruses in the WHO European Region in 2019/20 - impact of the COVID-19 pandemic.

The COVID-19 pandemic negatively impacted the 2019/20 WHO European Region influenza surveillance. Compared with previous 4-year averages, antigenic and genetic characterisations decreased by 17% (3,140 vs 2,601) and 24% (4,474 vs 3,403). Of subtyped influenza A viruses, 56% (26,477/47,357) were A(H1)pdm09, 44% (20,880/47,357) A(H3). Of characterised B viruses, 98% (4,585/4,679) were B/Victoria. Considerable numbers of viruses antigenically differed from northern hemisphere vaccine components. In 2020/21, maintaining influenza virological surveillance, while supporting SARS-CoV-2 surveillance is crucial.

COVID-19 Australia: Epidemiology Report 27: Fortnightly reporting period ending 11 October 2020.

Nationally, there was a continuing downward trend in notifications of COVID-19. The daily average number of cases for this reporting period was 14 compared to an average of 23 cases per day in the previous fortnight. There were 192 cases of COVID-19 and 23 deaths this fortnight, bringing the cumulative case count to 27,344 and 898 deaths. While the majority of cases in this reporting period were from Victoria (60%; 116/192), there continues to be a decrease in cases in this state resulting from public health interventions. During this fortnight, 66% (127/192) of all cases were reported as locally acquired, with the majority reported from Victoria (108/127). The highest proportion of overseas-acquired cases was reported in New South Wales (75%; 38/51), followed by Western Australia (22%; 11/51). Although testing rates declined, they remain high overall at 9.2 tests per week per 1,000 persons. There was variability in the testing rate by jurisdiction, with testing rates depending on the epidemic context. The overall positivity rate for the reporting period was 0.05%, with Victoria reporting a positivity rate of 0.08% for this reporting period. In all other jurisdictions the positivity rate was ≤ 0.06%.