Distributed lag inspired machine learning for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit admission.

Distributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates. Importance of a lagged feature in DLM is examined by hypothesis testing and a subset of important features are selected by evaluating an information criterion. Akin to the DLM, we demonstrate the selection of distributed lags in machine learning by evaluating importance scores and objective functions. Finally, we apply the DLIML with supervised learning for forecasting daily changes in COVID-19 hospitalization and ICU admission rates in United Kingdom (UK) and United States of America (USA). A sharp decline in hospitalization and ICU admission rates are observed when around 40% people are vaccinated. For one percent more vaccination, daily changes in hospitalization and ICU admission rates are expected to reduce by 4.05 and 0.74 per million after 14 days in UK, and 5.98 and 1.04 per million after 20 days in USA, respectively. Long-run cumulative effects in the DLM demonstrate that the daily changes in hospitalization and ICU admission rates are expected to jitter around the zero line in a long-run. Application of the DLIML selects fewer lagged features but provides qualitatively better forecasting outcome for data-driven healthcare service planning.

Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis.

Corruption-income inequality nexus is likely to affect the healthcare services, which in turn affect a country's ability to suppress an epidemic. Widespread corruption in public sectors may influence the data inventory practices to control the recording and sharing of official statistics to avoid political disturbance or social problems caused by an epidemic. This empirical study examines the effects of income inequality, data inventory, and universal healthcare coverage on cross-country variation in reported numbers of COVID-19 cases and deaths in the presence of corruption in public sectors. Daily numbers of COVID-19 cases and deaths of selected 29 countries are integrated for the first 120 days of the epidemic in each country. COVID-19 dataset is then integrated with a dataset of different indices. Fixed effect panel model is applied to explore the effects of corruption perception, income inequality, open data inventory practice, and universal health coverage on the daily numbers of COVID-19 cases and deaths per million. Income inequality, corruption perception and open data inventory are found to significantly affect the number of confirmed cases and deaths. Countries with alarming income inequality are found to report 39.89 more COVID-19 cases per million, on average. Under a lower level of corruption, countries with lower level of open data inventory are expected to report 74.31 more COVID-19 cases but 1.43 less deaths per million. Given a higher level of corruption, countries with lower level of open data inventory are expected to report lower number of COVID-19 cases and deaths. Corruption demonstrates a significant influence on the size of the epidemic in terms of the number of COVID-19 cases and deaths. A country with higher level of corruption in public sector along with lower levels of open data inventory is expected to report lower number of COVID-19 cases and deaths.

Assessing privacy risks in population health publications using a checklist-based approach.

OBJECTIVE: Recent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users identify the same privacy risks on applying the checklist to a sample of publications. METHODS: The checklist was applied to a sample of 100 academic population health publications distributed among 5 readers. Cohen's κ was used to measure interrater agreement. RESULTS: Of the 566 instances of statistical output types found in the 100 publications, the most frequently occurring were counts, summary statistics, plots, and model outputs. Application of the checklist identified 128 outputs (22.6%) with potential privacy concerns. Most of these were associated with the reporting of small counts. Among these identified outputs, the readers found no substantial actual privacy concerns when context was taken into account. Interrater agreement for identifying potential privacy concerns was generally good. CONCLUSION: This study has demonstrated that a checklist can be a reliable tool to assist researchers with anonymizing analysis outputs in population health research. This further suggests that such an approach may have the potential to be developed into a broadly applicable standard providing consistent confidentiality protection across multiple analyses of the same data.