Intelligent Decision-Support System for Epidemiological Diagnostics. I. A Concept of Architecture Design

The problems of decision support for epidemiological diagnostics are investigated. The basis for supporting decision-making is mathematical tools for analyzing morbidity data, as well as modeling of epidemic processes. The current state of research in this area is analyzed. The features of decision-making in epidemiology and public health are formalized. Principles for the development of an intelligent information system for decision-making support for epidemiological diagnostics are proposed. A systemic model of the system, a model of the interaction of elements of the epidemiological diagnostics system and the interaction of logical components of the information system has been developed. Taking into account the identified features of these processes, the concept of the architecture of such an intelligent information system is proposed.

Nowcasting COVID-19 deaths in England by age and region

Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting-day effects and longer-term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.

Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

Objectives

The internet data is an essential tool for reflecting public attention to hot issues. This study aimed to use the Baidu Index (BDI) and Sina Micro Index (SMI) to confirm correlation between COVID-19 case data and Chinese online data (public attention). This could verify the effect of online data on early warning of public health events, which will enable us to respond in a more timely and effective manner.

Methods

Spearman correlation was used to check the consistency of BDI and SMI. Time lag cross-correlation analysis of BDI, SMI and six case-related indicators and multiple linear regression prediction were performed to explore the correlation between public concern and the actual epidemic.

Results

The public's usage trend of the Baidu search engine and Sina Weibo was consistent during the COVID-19 outbreak. BDI, SMI and COVID-19 indicators had significant advance or lag effects, among which SMI and six indicators all had advance effects while BDI only had advance effects with new confirmed cases and new death cases. But compared with the SMI, the BDI was more closely related to the epidemic severity. Notably, the prediction model constructed by BDI and SMI can well fit new confirmed cases and new death cases.

Conclusions

The confirmed associations between the public's attention to the outbreak of COVID and the trend of epidemic outbreaks implied valuable insights into effective mechanisms of crisis response. In response to public health emergencies, people can through the information recommendation functions of social media and search engines (such as Weibo hot search and Baidu homepage recommendation) to raise awareness of available disease prevention and treatment, health services, and policy change.

Monitoring and Analysis of COVID-19 Pandemic: The Need for an Empirical Approach

The current worldwide pandemic produced by coronavirus disease 2019 (COVID-19) has changed the paradigm of mathematical epidemiology due to the high number of unknowns of this new disease. Thus, the empirical approach has emerged as a robust tool to analyze the actual situation carried by the countries and also allows us to predict the incoming scenarios. In this paper, we propose three empirical indexes to estimate the state of the pandemic. These indexes quantify both the propagation and the number of estimated cases, allowing us to accurately determine the real risk of a country. We have calculated these indexes' evolution for several European countries. Risk diagrams are introduced as a tool to visualize the evolution of a country and evaluate its current risk as a function of the number of contagious individuals and the empiric reproduction number. Risk diagrams at the regional level are useful to observe heterogeneity on COVID-19 penetration and spreading in some countries, which is essential during deconfinement processes. During the pandemic, there have been significant differences seen in countries reporting case criterion and detection capacity. Therefore, we have introduced estimations about the real number of infectious cases that allows us to have a broader view and to better estimate the risk. These diagrams and indexes have been successfully used for the monitoring of European countries and regions during the COVID-19 pandemic.

Estimation of SARS-CoV-2 Infection Fatality Rate by Real-time Antibody Screening of Blood Donors

Background

The pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has tremendous consequences for our societies. Knowledge of the seroprevalence of SARS-CoV-2 is needed to accurately monitor the spread of the epidemic and to calculate the infection fatality rate (IFR). These measures may help the authorities make informed decisions and adjust the current societal interventions. The objective was to perform nationwide real-time seroprevalence surveying among blood donors as a tool to estimate previous SARS-CoV-2 infections and the population-based IFR.

Methods

Danish blood donors aged 17–69 years giving blood 6 April to 3 May were tested for SARS-CoV-2 immunoglobulin M and G antibodies using a commercial lateral flow test. Antibody status was compared between geographical areas, and an estimate of the IFR was calculated. Seroprevalence was adjusted for assay sensitivity and specificity taking the uncertainties of the test validation into account when reporting the 95% confidence intervals (CIs).

Results

The first 20 640 blood donors were tested, and a combined adjusted seroprevalence of 1.9% (95% CI, .8–2.3) was calculated. The seroprevalence differed across areas. Using available data on fatalities and population numbers, a combined IFR in patients <70 years is estimated at 89 per 100 000 (95% CI, 72–211) infections.

Conclusions

The IFR was estimated to be slightly lower than previously reported from other countries not using seroprevalence data. The IFR is likely severalfold lower than the current estimate. We have initiated real-time nationwide anti–SARS-CoV-2 seroprevalence surveying of blood donations as a tool in monitoring the epidemic.

Estimation of SARS-CoV-2 Infection Fatality Rate by Real-time Antibody Screening of Blood Donors

BACKGROUND

The pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has tremendous consequences for our societies. Knowledge of the seroprevalence of SARS-CoV-2 is needed to accurately monitor the spread of the epidemic and to calculate the infection fatality rate (IFR). These measures may help the authorities make informed decisions and adjust the current societal interventions. The objective was to perform nationwide real-time seroprevalence surveying among blood donors as a tool to estimate previous SARS-CoV-2 infections and the population-based IFR.

METHODS

Danish blood donors aged 17-69 years giving blood 6 April to 3 May were tested for SARS-CoV-2 immunoglobulin M and G antibodies using a commercial lateral flow test. Antibody status was compared between geographical areas, and an estimate of the IFR was calculated. Seroprevalence was adjusted for assay sensitivity and specificity taking the uncertainties of the test validation into account when reporting the 95% confidence intervals (CIs).

RESULTS

The first 20 640 blood donors were tested, and a combined adjusted seroprevalence of 1.9% (95% CI, .8-2.3) was calculated. The seroprevalence differed across areas. Using available data on fatalities and population numbers, a combined IFR in patients <70 years is estimated at 89 per 100 000 (95% CI, 72-211) infections.

CONCLUSIONS

The IFR was estimated to be slightly lower than previously reported from other countries not using seroprevalence data. The IFR is likely severalfold lower than the current estimate. We have initiated real-time nationwide anti-SARS-CoV-2 seroprevalence surveying of blood donations as a tool in monitoring the epidemic.

Estimating populations of men who have sex with men in the southern United States

Population estimates of men who have sex with men (MSM) by state and race/ethnicity are lacking, hampering effective HIV epidemic monitoring and targeting of outreach and prevention efforts. We created three models to estimate the proportion and number of adult males who are MSM in 17 southern states. Model A used state-specific census data stratified by rural/suburban/urban area and national estimates of the percentage MSM in corresponding areas. Model B used a national estimate of the percentage MSM and state-specific household census data. Model C partitioned the statewide estimates by race/ethnicity. Statewide Models A and B estimates of the percentages MSM were strongly correlated (r = 0.74; r-squared = 0.55; p < 0.001) and had similar means (5.82% and 5.88%, respectively) and medians (5.5% and 5.2%, respectively). The estimated percentage MSM in the South was 6.0% (range 3.6-13.2%; median, 5.4%). The combined estimated number of MSM was 2.4 million, including 1,656,500 (69%) whites, 339,400 (14%) blacks, 368,800 (15%) Hispanics, 34,600 (1.4%) Asian/Pacific Islanders, 7,700 (0.3%) American Indians/Alaska Natives, and 11,000 (0.5%) others. The estimates showed considerable variability in state-specific racial/ethnic percentages MSM. MSM population estimates enable better assessment of community vulnerability, HIV/AIDS surveillance, and allocation of resources. Data availability and computational ease of our models suggest other states could similarly estimate their MSM populations.