Leveraging human resources for outbreak analysis: lessons from an international collaboration to support the sub-Saharan African COVID-19 response.

Emerging infectious diseases are a growing threat in sub-Saharan African countries, but the human and technical capacity to quickly respond to outbreaks remains limited. Here, we describe the experience and lessons learned from a joint project with the WHO Regional Office for Africa (WHO AFRO) to support the sub-Saharan African COVID-19 response.In June 2020, WHO AFRO contracted a number of consultants to reinforce the COVID-19 response in member states by providing actionable epidemiological analysis. Given the urgency of the situation and the magnitude of work required, we recruited a worldwide network of field experts, academics and students in the areas of public health, data science and social science to support the effort. Most analyses were performed on a merged line list of COVID-19 cases using a reverse engineering model (line listing built using data extracted from national situation reports shared by countries with the Regional Office for Africa as per the IHR (2005) obligations). The data analysis platform The Renku Project ( https://renkulab.io ) provided secure data storage and permitted collaborative coding.Over a period of 6 months, 63 contributors from 32 nations (including 17 African countries) participated in the project. A total of 45 in-depth country-specific epidemiological reports and data quality reports were prepared for 28 countries. Spatial transmission and mortality risk indices were developed for 23 countries. Text and video-based training modules were developed to integrate and mentor new members. The team also began to develop EpiGraph Hub, a web application that automates the generation of reports similar to those we created, and includes more advanced data analyses features (e.g. mathematical models, geospatial analyses) to deliver real-time, actionable results to decision-makers.Within a short period, we implemented a global collaborative approach to health data management and analyses to advance national responses to health emergencies and outbreaks. The interdisciplinary team, the hands-on training and mentoring, and the participation of local researchers were key to the success of this initiative.

Association of institutional masking policies with healthcare-associated SARS-CoV-2 infections in Swiss acute care hospitals during the BA.4/5 wave (CH-SUR study): a retrospective observational study.

BACKGROUND: In the initial phase of the SARS-CoV-2 pandemic, masking has been widely accepted in healthcare institutions to mitigate the risk of healthcare-associated infection. Evidence, however, is still scant and the role of masks in preventing healthcare-associated SARS-CoV-2 acquisition remains unclear.We investigated the association of variation in institutional mask policies with healthcare-associated SARS-CoV-2 infections in acute care hospitals in Switzerland during the BA.4/5 2022 wave. METHODS: SARS-CoV-2 infections in hospitalized patients between June 1 and September 5, 2022, were obtained from the "Hospital-based surveillance of COVID-19 in Switzerland"-database and classified as healthcare- or community-associated based on time of disease onset. Institutions provided information regarding institutional masking policies for healthcare workers and other prevention policies. The percentage of healthcare-associated SARS-CoV-2 infections was calculated per institution and per type of mask policy. The association of healthcare-associated SARS-CoV-2 infections with mask policies was tested using a negative binominal mixed-effect model. RESULTS: We included 2'980 SARS-CoV-2 infections from 13 institutions, 444 (15%) were classified as healthcare-associated. Between June 20 and June 30, 2022, six (46%) institutions switched to a more stringent mask policy. The percentage of healthcare-associated infections subsequently declined in institutions with policy switch but not in the others. In particular, the switch from situative masking (standard precautions) to general masking of HCW in contact with patients was followed by a strong reduction of healthcare-associated infections (rate ratio 0.39, 95% CI 0.30-0.49). In contrast, when compared across hospitals, the percentage of health-care associated infections was not related to mask policies. CONCLUSIONS: Our findings suggest switching to a more stringent mask policy may be beneficial during increases of healthcare-associated SARS-CoV-2 infections at an institutional level.

Urban-Rural Gaps in Breastfeeding Practices: Evidence From Lao People's Democratic Republic.

Objectives: Breastfeeding rates are decreasing rapidly in many low and middle-income countries, disproportionately affecting urban residences. We use data from Lao People's Democratic Republic to identify primary mechanisms underlying the urban-rural gap in breastfeeding practices. Methods: We used data from the 2017 Lao Social Indicator Survey II. Residence was categorized as large-urban (>1 million), small-urban (<1 Million), and rural. Multivariable logistic regression provided odds ratios and 95% confidence intervals (CI) to identify factors attributing to the urban-rural differences in complying with World Health Organization's breastfeeding recommendations for children <24 months. Results: Mothers in large-urban residences had 3.78 (95% confidence intervals: 1.19, 11.95) and 4.67 (95% CI: 2.30, 9.46) higher odds of non-compliance with exclusive and complementary breastfeeding recommendations, respectively, than mothers living in rural areas in bivariate models. Breastfeeding differentials between small urban and rural residences were largely explained by differences in maternal education and household wealth. Conclusion: Results of our paper suggest large disparities in breastfeeding practices between large-urban, small-urban, and rural residences.

Understanding the Dynamics of the COVID-19 Pandemic: A Real-Time Analysis of Switzerland's First Wave.

Since the novel coronavirus outbreak of SARS-CoV-2 from the first cases whereof were reported in Wuhan, China, in December 2019, our globalized world has changed enormously. On the 11th of March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic, and nations around the world have taken drastic measures to reduce transmission of the disease. The situation is similar in Switzerland, a small high-income country in Central Europe, where the first COVID-19 case was registered on the 25th of February 2020. Through literature review as well as correspondence with public health professionals and experts in mathematical modeling, this case study focuses on the outbreak's impact on Switzerland and on the measures this country has implemented thus far. Along with the rapid spread of the virus, the political organization, economy, healthcare system, and characteristics of the country greatly influence the approach taken in facing the crisis. Switzerland appears to be structurally well-prepared, but, according to mathematical modeling predictions, in order to avoid total collapse of healthcare facilities, the measures taken by the Swiss Government need to reduce the virus transmission chain by at least 70%. Fortunately, updated models on April 22nd show evidence that the non-pharmaceutical measures invoked have decreased transmission by an estimated 89%, proving effective management by the federal government and allowing for progressive deconfinement measures.