The Ebola Crisis and the Corresponding Public Behavior: A System Dynamics Approach

Global sensitivity analysis in epidemiological modeling

Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.

Hepatitis C virus risk among young people who inject drugs

Background

Injection drug use (IDU) is the leading risk factor for hepatitis C virus (HCV) transmission in the U.S. While the general risk factors for HCV transmission are known, there is limited work on how these factors interact and impact young people who inject drugs (YPWID).

Methods

Project data were drawn from a study of 539 New York City (NYC) residents ages 18-29 who were recruited via Respondent-Driven Sampling and, reported past-month non-medical use of prescription opioids and/or heroin. Analyses are based on a subsample of 337 (62%) who reported injecting any drug in the past 12 months. All variables were assessed via self-report, except HCV status, which was established via rapid antibody testing. Integrating the observed statistical associations with extant literature on HCV risk, we also developed a qualitative system dynamics (SD) model to use as a supplemental data visualization tool to explore plausible pathways and interactions among key risk and protective factors for HCV.

Results

Results showed a 31% HCV antibody prevalence with an overall incidence of 10 per 100 person-years. HCV status was independently correlated with having shared cookers with two or more people (AOR = 2.17); injected drugs 4–6 years (AOR = 2.49) and 7 or more years (AOR = 4.95); lifetime homelessness (AOR = 2.52); and having been incarcerated two or more times (AOR = 1.99). These outcomes along with the extant literature on HCV risk were used to develop the qualitative SD model, which describes a causal hypothesis around non-linearities and feedback loop structures underlying the spread of HCV among YPWID.

Conclusions

Despite ongoing harm reduction efforts, close to a third of YPWID in the community sample have been exposed to HCV, have risks for injection drug use, and face challenges with structural factors that may be preventing adequate intervention. The qualitative SD model explores these issues and contributes to a better understanding of how these various risk factors interact and what policies could potentially be effective in reducing HCV infections.

On the effectiveness of communication strategies as non-pharmaceutical interventions to tackle epidemics

When pharmaceutical interventions are unavailable to deal with an epidemic outbreak, adequate management of communication strategies can be key to reduce the contagion risks. On the one hand, accessibility to trustworthy and timely information, whilst on the other, the adoption of preventive behaviors may be both crucial. However, despite the abundance of communication strategies, their effectiveness has been scarcely evaluated or merely circumscribed to the scrutiny of public affairs. To study the influence of communication strategies on the spreading dynamics of an infectious disease, we implemented a susceptible-exposed-infected-removed-dead (SEIRD) epidemiological model, using an agent-based approach. Agents in our systems can obtain information modulating their behavior from two sources: (i) through the local interaction with other neighboring agents and, (ii) from a central entity delivering information with a certain periodicity. In doing so, we highlight how global information delivered from a central entity can reduce the impact of an infectious disease and how informing even a small fraction of the population has a remarkable impact, when compared to not informing the population at all. Moreover, having a scheme of delivering daily messages makes a stark difference on the reduction of cases, compared to the other evaluated strategies, denoting that daily delivery of information produces the largest decrease in the number of cases. Furthermore, when the information spreading relies only on local interactions between agents, and no central entity takes actions along the dynamics, then the epidemic spreading is virtually independent of the initial amount of informed agents. On top of that, we found that local communication plays an important role in an intermediate regime where information coming from a central entity is scarce. As a whole, our results highlight the importance of proper communication strategies, both accurate and daily, to tackle epidemic outbreaks.

A multi-method approach to modeling COVID-19 disease dynamics in the United States

In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.

The COVID-19 epidemic and evaluating the corresponding responses to crisis management in refugees: a system dynamic approach

Purpose

The COVID-19 pandemic is a new crisis in the world that caused many restrictions, from personal life to social and business. In this situation, the most vulnerable groups such as refugees who are living in the camps are faced with more serious problems. Therefore, a system dynamic approach has been developed to evaluate the effect of applying different scenarios to find out the best response to COVID-19 to improve refugees’ health and education.

Design/methodology/approach

The interaction of several health and education factors during an epidemic crisis among refugees leads to behavioral responses that consequently make the crisis control a complex problem. This research has developed an SD model based on the SIER model that responds to the public health and education system of Syrian refugees in Turkey affected by the COVID-19 virus and considered three policies of isolation, social distance/hygiene behavior and financial aid using the available data from various references.

Findings

The findings from the SD simulation results of applying three different policies identify that public health and education systems can increase much more by implementing the policy of social distance/hygiene behavior, and it has a significant impact on the control of the epidemic in comparison with the other two responses.

Originality/value

This paper contributes to humanitarian organizations, governments and refugees by discussing useful insights. Implementing the policy of social distance and hygiene behavior policies would help in a sharp reduction of death in refugees group. and public financial support has improved distance education during this pandemic.

The Effect of Ebola Virus Disease on Maternal and Child Health Services and Child Mortality in Sierra Leone, 2014-2015: Implications for COVID-19

During Sierra Leone's 2014-2015 Ebola virus disease (EVD) epidemic, early reports warned of health system collapse and potential effects on other-cause mortality. These same warnings are reverberating during the COVID-19 pandemic. Consideration of the impacts of EVD on maternal and child health services from facility data can be instructive during COVID-19. We surveyed all peripheral healthcare units (PHUs) in Sierra Leone in October 2014 and March 2015 to assess closures, staffing, amenities, medicines, supplies, and service utilization during May 2014-January 2015 and October 2013-January 2014. We report PHU characteristics and service utilization changes for equivalent 4-month periods during the epidemic and the prior year. We present utilization changes by district and service type, and model excess child mortality. PHU closures (-8%) and staff attrition (-3%) were limited, but many facilities lacked amenities, medicines, and supplies. Utilization of preventive and scheduled services fell more than individualized, clinical care interventions, aside from malaria treatment which declined significantly. Ebola virus disease intensity in districts was weakly associated with utilization, aside from two districts that were severely affected. Modeling suggests utilization declines resulted in 6,782 excess under-five deaths (an increase of 21%) between 2014 and 2015. Ebola virus disease negatively affected service provision, but utilization declined relatively more, particularly for preventive and scheduled interventions. Although these findings are specific to Sierra Leone's EVD epidemic, they illustrate the magnitude of possible effects in other settings due to COVID-19-induced service disruptions, where collateral impacts on child mortality from other preventable causes may far outweigh COVID-19 mortality.

Applied systems thinking: a viable approach to identify leverage points for accelerating progress towards ending neglected tropical diseases

BACKGROUND

Systems thinking is a conceptual approach that can assist stakeholders in understanding complexity and making progress on persistent public health challenges. Neglected tropical diseases (NTDs), a complex global health problem, are responsible for a large disease burden among impoverished populations around the world. This aim of this study was to better discern the many complexities of the global NTD system in order to identify and act on leverage points to catalyse progress towards ending NTDs.

METHODS

Existing frameworks for systems change were adapted to form the conceptual framework for the study. Using a semi-structured interview guide, key informant interviews were conducted with NTD stakeholders at the global level and at the country level in Nigeria. The interview data were coded and analysed to create causal loop diagrams that resulted in a qualitative model of the global NTD system.

RESULTS

The complete qualitative model is discussed and presented visually as six separate sub-components that highlight key forces and feedback loops within the global NTD system.

CONCLUSIONS

We identified five leverage points for NTD system change, namely (1) clarify the potential for and assess realistic progress towards NTD elimination, (2) increase support for interventions besides drug delivery, (3) reduce dependency on international donors, (4) create a less insular culture within the global NTD community, and (5) systemically address the issue of health worker incentives. The specific findings for NTDs raise a number of uncomfortable questions that have not been addressed, at least in part, because it is easier to continue focusing on 'quick win' solutions. The study provides a model of a systems thinking approach that can be applied to other complex global health and development challenges in order to understand complexity and identify leverage points for system change.

Spatial dynamics of access to primary care for the medicaid population

Primary care (PC) has always been underestimated and underinvested by the United States health system. Our goal was to investigate the effect of Medicaid expansion and the Affordable Care Act (ACA) provisions on PC access in Broome County, NY, a county that includes both rural and urban areas, and can serve as a benchmark for other regions. We developed a spatial system dynamics model to capture different stages of PC access for the Medicaid population by using the health belief model constructs and simulate the effect of several hypothetical interventions on PC utilisation. The government data portals used as data sources for calibrating our model include the New York State Department of Health, the Medicaid Delivery System Reform Incentive Payment (DSRIP) dashboards, and the US census. In our unique approach, we integrated the simulation results within Geographical Information System (GIS) maps, to assess the influence of geospatial factors on PC access. Our results identify hot spot demographic areas that have poor access to PC service facilities due to transportation constraints and a shortage in PC providers. Our decision support tool informs policymakers about programmes with the strongest impact on improving access to care, considering spatial and temporal characteristics of a region.

System dynamics modelling approach to explore the effect of dog demography on rabies vaccination coverage in Africa

BACKGROUND

Dogs are important reservoirs of rabies, a zoonotic viral encephalitis that kills thousands of people in Asia and Africa annually. Mass dog vaccination is recommended for the prevention of rabies in both humans and dogs. Mass vaccinations should be conducted annually but more frequent campaigns may be required in areas with high dog turnover rates. Consequently, a good understanding of dog demography is essential for the control of the disease. The aim of this study was to explore the effect of dog demography on rabies vaccination coverage following a mass vaccination campaign with at least 70% vaccination coverage.

METHODOLOGY/PRINCIPAL FINDINGS

A dog population model was constructed to explore the effect of dog demography on rabies vaccination coverage decline. Important model parameters were identified through a comprehensive literature search on dog demography in Africa. A system dynamics approach was adopted to build a dog population model to simulate the effects of demographic processes on rabies vaccination coverage. Vensim PLE Plus software was used to construct the model. Multivariate sensitivity simulations using data from 22 studies and 12 African countries were performed to investigate the effect of dog turnover rates on vaccination coverage decline. In addition, an adjusted vaccination coverage to estimate the proportion of vaccinated dogs with adequate immunity at 12 months post-vaccination was calculated. The results demonstrated that the vaccination coverage and adjusted vaccination coverage remained over 30% and 20% respectively at 12 months if annual mass vaccinations achieved at least 70% coverage.

CONCLUSIONS/SIGNIFICANCE

The results demonstrated that annual mass vaccination campaigns with at least 70% vaccination coverage would maintain a herd immunity of 20‒45% between campaigns.

Ebola in the Netherlands, 2014-2015: costs of preparedness and response

The recent epidemic of Ebola virus disease (EVD) resulted in countries worldwide to prepare for the possibility of having an EVD patient. In this study, we estimate the costs of Ebola preparedness and response borne by the Dutch health system. An activity-based costing method was used, in which the cost of staff time spent in preparedness and response activities was calculated based on a time-recording system and interviews with key professionals at the healthcare organizations involved. In addition, the organizations provided cost information on patient days of hospitalization, laboratory tests, personal protective equipment (PPE), as well as the additional cleaning and disinfection required. The estimated total costs averaged €12.6 million, ranging from €6.7 to €22.5 million. The main cost drivers were PPE expenditures and preparedness activities of personnel, especially those associated with ambulance services and hospitals. There were 13 possible cases clinically evaluated and one confirmed case admitted to hospital. The estimated total cost of EVD preparedness and response in the Netherlands was substantial. Future costs might be reduced and efficiency increased by designating one ambulance service for transportation and fewer hospitals for the assessment of possible patients with a highly infectious disease of high consequences.