Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts

Epidemiological Consequences of Individual Centrality on Wild Chimpanzees

Disease outbreaks are one of the key threats to great apes and other wildlife. Because the spread of some pathogens (e.g., respiratory viruses, sexually transmitted diseases, ectoparasites) are mediated by social interactions, there is a growing interest in understanding how social networks predict the chain of pathogen transmission. In this study, we built a party network from wild chimpanzees (Pan troglodytes), and used agent-based modeling to test: (i) whether individual attributes (sex, age) predict individual centrality (i.e., whether it is more or less socially connected); (ii) whether individual centrality affects an individual's role in the chain of pathogen transmission; and, (iii) whether the basic reproduction number (R0) and infectious period modulate the influence of centrality on pathogen transmission. We show that sex and age predict individual centrality, with older males presenting many (degree centrality) and strong (strength centrality) relationships. As expected, males are more central than females within their network, and their centrality determines their probability of getting infected during simulated outbreaks. We then demonstrate that direct measures of social interaction (strength centrality), as well as eigenvector centrality, strongly predict disease dynamics in the chimpanzee community. Finally, we show that this predictive power depends on the pathogen's R0 and infectious period: individual centrality was most predictive in simulations with the most transmissible pathogens and long-lasting diseases. These findings highlight the importance of considering animal social networks when investigating disease outbreaks.

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study

INTRODUCTION

The 2019 novel coronavirus (COVID-19) has been recognized as the most severe human infectious disease pandemic in the past century. To enhance our ability to control potential infectious diseases in the future, this study simulated the influence of nucleic acid testing on the transmission of COVID-19 across varied scenarios. Additionally, it assessed the demand for nucleic acid testing under different circumstances, aiming to furnish a decision-making foundation for the implementation of nucleic acid screening measures, the provision of emergency materials, and the allocation of human resources.

METHODS

Considering the transmission dynamics of COVID-19 and the preventive measures implemented by countries, we explored three distinct levels of epidemic intensity: community transmission, outbreak, and sporadic cases. Integrating the theory of scenario analysis, we formulated six hypothetical epidemic scenarios, each corresponding to possible occurrences during different phases of the pandemic. We developed an improved SEIR model, validated its accuracy using real-world data, and conducted a comprehensive analysis and prediction of COVID-19 infections under these six scenarios. Simultaneously, we assessed the testing resource requirements associated with each scenario.

RESULTS

We compared the predicted number of infections simulated by the modified SEIR model with the actual reported cases in Israel to validate the model. The root mean square error (RMSE) was 350.09, and the R-squared (R2) was 0.99, indicating a well-fitted model. Scenario 4 demonstrated the most effective prevention and control outcomes. Strengthening non-pharmaceutical interventions and increasing nucleic acid testing frequency, even under low testing capacity, resulted in a delayed epidemic peak by 78 days. The proportion of undetected cases decreased from 77.83% to 31.21%, and the overall testing demand significantly decreased, meeting maximum demand even with low testing capacity. The initiation of testing influenced case detection probability. Under high testing capacity, increasing testing frequency elevated the detection rate from 36.40% to 77.83%. Nucleic acid screening proved effective in reducing the demand for testing resources under diverse epidemic prevention and control strategies. While effective interventions and nucleic acid screening measures substantially diminished the demand for testing-related resources, varying degrees of insufficient testing capacity may still persist.

CONCLUSIONS

The nucleic acid detection strategy proves effective in promptly identifying and isolating infected individuals, thereby mitigating the infection peak and extending the time to peak. In situations with constrained testing capacity, implementing more stringent measures can notably decrease the number of infections and alleviate resource demands. The improved SEIR model demonstrates proficiency in predicting both reported and unreported cases, offering valuable insights for future infection risk assessments. Rapid evaluations of testing requirements across diverse scenarios can aptly address resource limitations in specific regions, offering substantial evidence for the formulation of future infectious disease testing strategies.

Optimal resource allocation model for COVID-19: a systematic review and meta-analysis

BACKGROUND

A lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control.

METHODS

A systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated.

RESULTS

A total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger's test showed no publication bias, and sensitivity analysis suggested robust results.

CONCLUSION

When the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.

Morbidity and mortality burden of COVID-19 in rural Madagascar: results from a longitudinal cohort and nested seroprevalence study

INTRODUCTION

Three years into the pandemic, there remains significant uncertainty about the true infection and mortality burden of COVID-19 in the World Health Organization Africa region. High quality, population-representative studies in Africa are rare and tend to be conducted in national capitals or large cities, leaving a substantial gap in our understanding of the impact of COVID-19 in rural, low-resource settings. Here, we estimated the spatio-temporal morbidity and mortality burden associated with COVID-19 in a rural health district of Madagascar until the first half of 2021.

METHODS

We integrated a nested seroprevalence study within a pre-existing longitudinal cohort conducted in a representative sample of 1600 households in Ifanadiana District, Madagascar. Socio-demographic and health information was collected in combination with dried blood spots for about 6500 individuals of all ages, which were analysed to detect IgG and IgM antibodies against four specific proteins of SARS-CoV-2 in a bead-based multiplex immunoassay. We evaluated spatio-temporal patterns in COVID-19 infection history and its associations with several geographic, socio-economic and demographic factors via logistic regressions.

RESULTS

Eighteen percent of people had been infected by April-June 2021, with seroprevalence increasing with individuals' age. COVID-19 primarily spread along the only paved road and in major towns during the first epidemic wave, subsequently spreading along secondary roads during the second wave to more remote areas. Wealthier individuals and those with occupations such as commerce and formal employment were at higher risk of being infected in the first wave. Adult mortality increased in 2020, particularly for older men for whom it nearly doubled up to nearly 40 deaths per 1000. Less than 10% of mortality in this period would be directly attributed to COVID-19 deaths if known infection fatality ratios are applied to observed seroprevalence in the district.

CONCLUSION

Our study provides a very granular understanding on COVID-19 transmission and mortality in a rural population of sub-Saharan Africa and suggests that the disease burden in these areas may have been substantially underestimated.