Strategies for containing Ebola in West Africa

The basic reproduction number (R0) of ebola virus disease: A systematic review and meta-analysis

BACKGROUND

Ebola virus disease (Ebola) is highly pathogenic, transmissible, and often deadly, with debilitating consequences. Superspreading within a cluster is also possible. In this study, we aim to document Ebola basic reproduction number (R0): the average number of new cases associated with an Ebola case in a completely susceptible population.

METHODS

We undertook a systematic review and meta-analysis. We searched PubMed, EMBASE, and Web of Science for studies published between 1976 and February 27, 2023. We also manually searched the reference lists of the reviewed studies to identify additional studies. We included studies that reported R0 during Ebola outbreaks in Africa. We excluded studies that reported only the effective reproduction number (Rt). Abstracting data from included studies was performed using a pilot-tested standard form. Two investigators reviewed the studies, extracted the data, and assessed quality. The pooled R0 was determined by a random-effects meta-analysis. R0 was stratified by country. We also estimated the theoretically required immunization coverage to reach herd-immunity using the formula of (1-1/R0) × 100 %.

RESULTS

The search yielded 2042 studies. We included 53 studies from six African countries in the systematic review providing 97 Ebola mean R0 estimates. 27 (with 46 data points) studies were included in the meta-analysis. The overall pooled mean Ebola R0 was 1.95 (95 % CI 1.74-2.15), with high heterogeneity (I2 = 99.99 %; τ2 = 0.38; and p < 0.001) and evidence of small-study effects (Egger's statistics: Z = 4.67; p < 0.001). Mean Ebola R0 values ranged from 1.2 to 10.0 in Nigeria, 1.1 to 7 in Guinea, 1.14 to 8.33 in Sierra Leone, 1.13 to 5 in Liberia, 1.2 to 5.2 in DR Congo, 1.34 to 2.7 in Uganda, and from 1.40 to 2.55 for all West African countries combined. Pooled mean Ebola R0 was 9.38 (95 % CI 4.16-14.59) in Nigeria, 3.31 (95 % CI 2.30-4.32) in DR Congo, 2.0 (95 % CI 1.25-2.76) in Uganda, 1.83 (95 % CI 1.61-2.05) in Liberia, 1.73 (95 % CI 1.47-2.0) in Sierra Leonne, and 1.44 (95 % CI 1.29-1.60) in Guinea. In theory, 50 % of the population needs to be vaccinated to achieve herd immunity, assuming that Ebola vaccine would be 100 % effective.

CONCLUSIONS

Ebola R0 varies widely across countries. Ebola has a much wider R0 range than is often claimed (1.3-2.0). It is possible for an Ebola index case to infect more than two susceptible individuals.

Identification of dynamical changes of rabies transmission under quarantine: Community-based measures towards rabies elimination

Quarantine has been long used as a public health response to emerging infectious diseases, particularly at the onset of an epidemic when the infected proportion of a population remains identifiable and logistically tractable. In theory, the same logic should apply to low-incidence infections; however, the application and impact of quarantine in low prevalence settings appears less common and lacks a formal analysis. Here, we present a quantitative framework using a series of progressively more biologically realistic models of canine rabies in domestic dogs and from dogs to humans, a suitable example system to characterize dynamical changes under varying levels of dog quarantine. We explicitly incorporate health-seeking behaviour data to inform the modelling of contact-tracing and exclusion of rabies suspect and probable dogs that can be identified through bite-histories of patients presenting at anti-rabies clinics. We find that a temporary quarantine of rabies suspect and probable dogs provides a powerful tool to curtail rabies transmission, especially in settings where optimal vaccination coverage is yet to be achieved, providing a critical stopgap to reduce the number of human and animal deaths due to rabid bites. We conclude that whilst comprehensive measures including sensitive surveillance and large-scale vaccination of dogs will be required to achieve disease elimination and sustained freedom given the persistent risk of rabies re-introductions, quarantine offers a low-cost community driven solution to intersectoral health burden.

Global dynamics of a compartmental model to assess the effect of transmission from deceased

During several epidemics, transmission from deceased people significantly contributed to disease spread, but mathematical analysis of this transmission has not been seen in the literature numerously. Transmission of Ebola during traditional burials was the most well-known example; however, there are several other diseases, such as hepatitis, plague or Nipah virus, that can potentially be transmitted from disease victims. This is especially true in the case of serious epidemics when healthcare is overwhelmed and the operative capacity of the health sector is diminished, such as seen during the COVID-19 pandemic. We present a compartmental model for the spread of a disease with an imperfect vaccine available, also considering transmission from deceased infected in general. The global dynamics of the system are completely described by constructing appropriate Lyapunov functions. To support our analytical results, we perform numerical simulations to assess the importance of transmission from the deceased, considering the data collected from three infectious diseases, Ebola virus disease, COVID-19, and Nipah fever.

Epidemiology and Transmission Dynamics of Viral Encephalitides in West Africa

Encephalitis is an inflammation of the brain, often caused by an autoimmune reaction, or in most cases because of a direct viral, bacterial, or parasitic infection. Viral encephalitides (VE) presents a significant public health concern globally, especially in West Africa. There are more than five hundred known arthropod-borne viruses (arboviruses), with over a hundred of them identified to cause encephalitic diseases in humans and animals, giving rise to a tremendous burden of the diseases and socioeconomic strains in tropical and subtropical regions worldwide. Despite their importance, few effective preventive and control measures in the form of vaccines and therapies are available, and when they are, their use is limited. These limitations are largely hinged on the paucity of information about the molecular epidemiology and transmission patterns of VE in West Africa. Here, we reviewed the transmission dynamics, molecular epidemiology, and the ecological drivers of VE in West Africa. Collectively, timely and accurate interventions are essential for encephalitic viral disease control. Moreover, the integrated health system approach, combining surveillance, vaccination, vector control, and community engagement, could be effective in preventing viral encephalitis globally.

A hierarchical intervention scheme based on epidemic severity in a community network

As there are no targeted medicines or vaccines for newly emerging infectious diseases, isolation among communities (villages, cities, or countries) is one of the most effective intervention measures. As such, the number of intercommunity edges ([Formula: see text]) becomes one of the most important factor in isolating a place since it is closely related to normal life. Unfortunately, how [Formula: see text] affects epidemic spread is still poorly understood. In this paper, we quantitatively analyzed the impact of [Formula: see text] on infectious disease transmission by establishing a four-dimensional [Formula: see text] edge-based compartmental model with two communities. The basic reproduction number [Formula: see text] is explicitly obtained subject to [Formula: see text] [Formula: see text]. Furthermore, according to [Formula: see text] with zero [Formula: see text], epidemics spread could be classified into two cases. When [Formula: see text] for the case 2, epidemics occur with at least one of the reproduction numbers within communities greater than one, and otherwise when [Formula: see text] for case 1, both reproduction numbers within communities are less than one. Remarkably, in case 1, whether epidemics break out strongly depends on intercommunity edges. Then, the outbreak threshold in regard to [Formula: see text] is also explicitly obtained, below which epidemics vanish, and otherwise break out. The above two cases form a severity-based hierarchical intervention scheme for epidemics. It is then applied to the SARS outbreak in Singapore, verifying the validity of our scheme. In addition, the final size of the system is gained by demonstrating the existence of positive equilibrium in a four-dimensional coupled system. Theoretical results are also validated through numerical simulation in networks with the Poisson and Power law distributions, respectively. Our results provide a new insight into controlling epidemics.

A community-based contact isolation strategy to reduce the spread of Ebola virus disease: an analysis of the 2018-2020 outbreak in the Democratic Republic of the Congo

INTRODUCTION

Despite tremendous progress in the development of diagnostics, vaccines and therapeutics for Ebola virus disease (EVD), challenges remain in the implementation of holistic strategies to rapidly curtail outbreaks. We investigated the effectiveness of a community-based contact isolation strategy to limit the spread of the disease in the Democratic Republic of Congo (DRC).

METHODS

We did a quasi-experimental comparison study. Eligible participants were EVD contacts registered from 12 June 2019 to 18 May 2020 in Beni and Mabalako Health Zones. Intervention group participants were isolated to specific community sites for the duration of their follow-up. Comparison group participants underwent contact tracing without isolation. The primary outcome was measured as the reproduction number (R) in the two groups. Secondary outcomes were the delay from symptom onset to isolation and case management, case fatality rate (CFR) and vaccination uptake.

RESULTS

27 324 EVD contacts were included in the study; 585 in the intervention group and 26 739 in the comparison group. The intervention group generated 32 confirmed cases (5.5%) in the first generation, while the comparison group generated 87 (0.3%). However, the 32 confirmed cases arising from the intervention contacts did not generate any additional transmission (R=0.00), whereas the 87 confirmed cases arising from the comparison group generated 99 secondary cases (R=1.14). The average delay between symptom onset and case isolation was shorter (1.3 vs 4.8 days; p<0.0001), CFR lower (12.5% vs 48.4%; p=0.0001) and postexposure vaccination uptake higher (86.0% vs 56.8%; p<0.0001) in the intervention group compared with the comparison group. A significant difference was also found between intervention and comparison groups in survival rate at the discharge of hospitalised confirmed patients (87.9% vs 47.7%, respectively; p=0.0004).

CONCLUSION

The community-based contact isolation strategy used in DRC shows promise as a potentially effective approach for the rapid cessation of EVD transmission, highlighting the importance of rapidly implemented, community-oriented and trust-building control strategies.

Parallel evolution and control method for predicting the effectiveness of non-pharmaceutical interventions in pandemics

Aim

Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making.

Subject and methods

Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics.

Results

The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds.

Conclusion

The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01843-2.

Predicting the combined effects of case isolation, safe funeral practices, and contact tracing during Ebola virus disease outbreaks

BACKGROUND

The recent outbreaks of Ebola virus disease (EVD) in Uganda and the Marburg virus disease (MVD) in Ghana reflect a persisting threat of Filoviridae to the global health community. Characteristic of Filoviridae are not just their high case fatality rates, but also that corpses are highly contagious and prone to cause infections in the absence of appropriate precautions. Vaccines against the most virulent Ebolavirus species, the Zaire ebolavirus (ZEBOV) are approved. However, there exists no approved vaccine or treatment against the Sudan ebolavirus (SUDV) which causes the current outbreak of EVD. Hence, the control of the outbreak relies on case isolation, safe funeral practices, and contact tracing. So far, the effectiveness of these control measures was studied only separately by epidemiological models, while the impact of their interaction is unclear.

METHODS AND FINDINGS

To sustain decision making in public health-emergency management, we introduce a predictive model to study the interaction of case isolation, safe funeral practices, and contact tracing. The model is a complex extension of an SEIR-type model, and serves as an epidemic preparedness tool. The model considers different phases of the EVD infections, the possibility of infections being treated in isolation (if appropriately diagnosed), in hospital (if not properly diagnosed), or at home (if the infected do not present to hospital for whatever reason). It is assumed that the corpses of those who died in isolation are buried with proper safety measures, while those who die outside isolation might be buried unsafely, such that transmission can occur during the funeral. Furthermore, the contacts of individuals in isolation will be traced. Based on parameter estimates from the scientific literature, the model suggests that proper diagnosis and hence isolation of cases has the highest impact in reducing the size of the outbreak. However, the combination of case isolation and safe funeral practices alone are insufficient to fully contain the epidemic under plausible parameters. This changes if these measures are combined with contact tracing. In addition, shortening the time to successfully trace back contacts contribute substantially to contain the outbreak.

CONCLUSIONS

In the absence of an approved vaccine and treatment, EVD management by proper and fast diagnostics in combination with epidemic awareness are fundamental. Awareness will particularly facilitate contact tracing and safe funeral practices. Moreover, proper and fast diagnostics are a major determinant of case isolation. The model introduced here is not just applicable to EVD, but also to other viral hemorrhagic fevers such as the MVD or the Lassa fever.

Design of control strategies to help prevent the spread of COVID-19 pandemic

This paper proposes control strategies to allocate COVID-19 patients to screening facilities, health facilities, and quarantine facilities for minimizing the spread of the virus by these patients. To calculate the transmission rate, we propose a function that accounts for contact rate, duration of the contact, age structure of the population, susceptibility to infection, and the number of transmission events per contact. Moreover, the COVID-19 cases are divided into different groups according to the severity of their disease and are allocated to appropriate health facilities that provide care tailored to their needs. The multi-stage fuzzy stochastic programming approach is applied to cope with uncertainty, in which the probability associated with nodes of the scenario tree is treated as fuzzy variables. To handle the probabilistic model, we use a more flexible measure, M e measure, which allows decision-makers to adopt varying attitudes by assigning the optimistic-pessimistic parameter. This measure does not force decision-makers to hold extreme views and obtain the interval solution that provides further information in the fuzzy environment. We apply the proposed model to the case of Tehran, Iran. The results of this study indicate that assigning patients to appropriate medical centers improves the performance of the healthcare system. The result analysis highlights the impact of the demographic differences on virus transmission, and the older population has a greater influence on virus transmission than other age groups. Besides, the results indicate that behavioral changes in the population and their vaccination play a key role in curbing COVID-19 transmission.

Machine learning based regional epidemic transmission risks precaution in digital society

The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.

Dynamic Hybrid Model to Forecast the Spread of COVID-19 Using LSTM and Behavioral Models Under Uncertainty

To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.

Modelling the Role of Human Behaviour in Ebola Virus Disease (EVD) Transmission Dynamics

The role of human behaviour in the dynamics of infectious diseases cannot be underestimated. A clear understanding of how human behaviour influences the spread of infectious diseases is critical in establishing and designing control measures. To study the role that human behaviour plays in Ebola disease dynamics, in this paper, we design an Ebola virus disease model with disease transmission dynamics based on a new exponential nonlinear incidence function. This new incidence function that captures the reduction in disease transmission due to human behaviour innovatively considers the efficacy and the speed of behaviour change. The model's steady states are determined and suitable Lyapunov functions are built. The proofs of the global stability of equilibrium points are presented. To demonstrate the utility of the model, we fit the model to Ebola virus disease data from Liberia and Sierra Leone. The results which are comparable to existing findings from the outbreak of 2014 − 2016 show a better fit when the efficacy and the speed of behaviour change are higher. A rapid and efficacious behaviour change as a control measure to rapidly control an Ebola virus disease epidemic is advocated. Consequently, this model has implications for the management and control of future Ebola virus disease outbreaks.

Individual risk-aversion responses tune epidemics to critical transmissibility (R = 1)

Changes in human behaviour are a major determinant of epidemic dynamics. Collective activity can be modified through imposed control measures, but spontaneous changes can also arise as a result of uncoordinated individual responses to the perceived risk of contagion. Here, we introduce a stochastic epidemic model implementing population responses driven by individual time-varying risk aversion. The model reveals an emergent mechanism for the generation of multiple infection waves of decreasing amplitude that progressively tune the effective reproduction number to its critical value R = 1. In successive waves, individuals with gradually lower risk propensity are infected. The overall mechanism shapes well-defined risk-aversion profiles over the whole population as the epidemic progresses. We conclude that uncoordinated changes in human behaviour can by themselves explain major qualitative and quantitative features of the epidemic process, like the emergence of multiple waves and the tendency to remain around R = 1 observed worldwide after the first few waves of COVID-19.

The influence of landscape structure on the dispersal pattern of yellow fever virus in the state of São Paulo

Man-made changes to the landscape play a crucial role in altering the epidemiologic patterns of infectious diseases, mainly as a result of pathogen spillover. Sylvatic yellow fever is ideally suited to modeling of this phenomenon as the risk of transmission of the disease as well as its circulation and dispersal are associated with forest fragmentation. In this study we investigated the temporal dispersal pattern of yellow fever virus (YFV) by means of confirmed cases of epizootics in non-human primates in municipalities in the state of São Paulo where there was no recommendation for vaccination in 2017. We analyzed the resistance to dispersal associated with different classes of land use and the geographic distances between the different locations where epizootics were recorded. The model that best explained the temporal dispersal pattern of YFV in the study area indicated that this was influenced by the geographic distance between collection locations and by the permeability of the forest edges (150 m) at the interface with the following core areas: Water, Agricultural, Non-Forest Formation and Forestry. Water, Agricultural, Urban and Forest core areas and the interfaces between the latter two formed important barriers to circulation of the virus. These findings indicate that fragmentation of vegetation tends to decrease the time taken for pathogens to spread, while conservation of forest areas has the opposite effect.

An Updated Analysis on the Risk Factors Associated with COVID-19 Transmission

BACKGROUND: The coronavirus disease 2019 (COVID-19) is a global public health disaster and knowledge of its associated risk factors provides protection/slowdown against its transmission. AIM: This study was undertaken to investigate all major risk factors associated with transmission of the COVID-19 infection. METHODS: The data on the risk associated factors for the COVID-19 transmission were collected from the Texas Medical Association, Center for Disease Prevention and Control, World Health Organization, and Health and Safety Executive. The collected data were combined, analyzed, and presented as percentage mean ± SD. RESULTS: The collective data showed that among games such as playing football and basketball are highly risky followed by swimming in public pool and playing at the beach. Whereas, playing golf and tennis are not risky (p < 0.05). Moreover, the carryout food from the restaurants is much safer as compared with eating at buffet, in restaurants (p < 0.01). The data on social gathering showed that religious places, sports stadium, music concert, cinema halls, amusement parks, attending funerals, and wedding showed a higher risk of spreading COVID-19. The data on general outing showed that going to gymnasium, traveling by bus or plane, and visiting in salon are highly risky (p < 0.01) for COVID-19 infection. Moreover, hugging, shaking hands, and kissing are also highly risky for the COVID-19 infection. CONCLUSIONS: This study provides the collective information on the risk factors associated with the COVID-19 transmission. The findings can contribute to the concerned authorities to formulate the preventive measures to limit spread of the COVID-19 infection.

The Kivu Ebola Epidemic

The Kivu Ebola epidemic began on 1 August 2018, when four cases of Ebola virus disease (EVD) were confirmed in the eastern region of Kivu in the Democratic Republic of the Congo (DRC). The disease affected the DRC, Uganda, and is suspected to have also affected Tanzania, though the Ministry of Health there never shared information with the WHO. The outbreak was declared ended on 25 June 2020, with a total of 3,470 cases and 2,280 deaths. Other locations in the DRC affected included the Ituri Province, where the first case was confirmed on 13 August 2018. In November 2018, it became the biggest Ebola outbreak in the DRC's history, and by November, it had become the second-largest Ebola outbreak in recorded history, behind only the 2013–2016 Western Africa epidemic. On 3 May 2019, 9 months into the outbreak, the DRC death toll surpassed 1,000. In June 2019, the virus reached Uganda, having infected a 5-year-old Congolese boy who entered with his family, but this was contained. Since January 2015, the affected province and general area have been experiencing a military conflict, which hindered treatment and prevention efforts. The World Health Organization (WHO) has described the combination of military conflict and civilian distress as a potential "perfect storm" that could lead to a rapid worsening of the situation. In May 2019, the WHO reported that, since January of that year, there had been 42 attacks on health facilities and 85 health workers had been wounded or killed. In some areas, aid organizations have had to stop their work due to violence. Health workers also had to deal with misinformation spread by opposing politicians. Due to the deteriorating security situation in North Kivu and surrounding areas, the WHO raised the risk assessment at the national and regional level from "high" to "very high" in September 2018. In October, the United Nations Security Council stressed that all armed hostility in the DRC should come to a stop to address the ongoing outbreak better. A confirmed case in Goma triggered the decision by the WHO to convene an emergency committee for the fourth time, and on 17 July 2019, the WHO announced a Public Health Emergency of International Concern (PHEIC), the highest level of alarm the WHO can sound. On 15 September 2019, some slowdown of cases was noted in the DRC. However, contact tracing continued to be less than 100%; at the time, it was at 89%. In mid-October the transmission of the virus had significantly reduced; by then it was confined to the Mandima region near where the outbreak began, and was only affecting 27 health zones in the DRC (down from a peak of 207). New cases decreased to zero by 17 February 2020, but after 52 days without a case, surveillance and response teams confirmed three new cases in mid-April. As a new and separate outbreak, was reported on 1 June 2020 in Équateur Province in north-western DRC, described as the eleventh Ebola outbreak since records began; after almost two years the tenth outbreak was declared ended on 25 June 2020, with a total of 3,470 cases and 2,280 deaths.

Community trust of government and non-governmental organizations during the 2014-16 Ebola epidemic in Liberia

The West African Ebola Virus Disease epidemic of 2014-16 cost more than 11,000 lives. Interventions targeting key behaviors to curb transmission, such as safe funeral practices and reporting and isolating the ill, were initially unsuccessful in a climate of fear, mistrust, and denial. Building trust was eventually recognized as essential to epidemic response and prioritized, and trust was seen to improve toward the end of the epidemic as incidence fell. However, little is understood about how and why trust changed during Ebola, what factors were most influential to community trust, and how different institutions might have been perceived under different levels of exposure to the outbreak. In this large-N household survey conducted in Liberia in 2018, we measured self-reported trust over time retrospectively in three different communities with different exposures to Ebola. We found trust was consistently higher for non-governmental organizations than for the government of Liberia across all time periods. Trust reportedly decreased significantly from the start to the peak of the epidemic in the study site of highest Ebola incidence. This finding, in combination with a negative association found between knowing someone infected and trust of both iNGOs and the government, indicates the experience of Ebola may have itself caused a decline of trust in the community. These results suggest that national governments should aim to establish trust when engaging communities to change behavior during epidemics. Further research on the relationship between trust and epidemics may serve to improve epidemic response efficacy and behavior uptake.

A nonlinear model predictive control model aimed at the epidemic spread with quarantine strategy

Allocating limit medicine resources by mathematical modeling to control spreading of epidemic diseases is a very promising approach. Especially, how to use the existing partial data to efficiently control epidemic diseases is a interesting problem. When an epidemic disease is spreading, it is very urgent and essential to build a prediction and control model based on the real-time and partial data in order that decision makers find and implement the optimal strategy timely. In this paper, we developed a new framework for solving the problem. Our nonlinear model predictive control (NMPC) based on a discrete time susceptible-infected-removed dynamics (SIR) gave an attempt that aims at timely dealing with the condition. Our NMPC model minimizes the total number of infectious cases and the total cost, with the treatment beds capacity constraints and other constraints, especially, with a state observer based on the system output which can be sampled more easily and more accurately. Our control policy can be updated timely according to the current statistical data because our NMPC is a kind of closed-loop control algorithm based on our observer. We also presented some theoretical results on the state observer. Finally, we gave a numerical example to illustrate our algorithm.

MODELING THE IMPACT OF EDUCATIONAL CAMPAIGN ON THE TRANSMISSION DYNAMICS OF EBOLA

Disease awareness that informs the public about the severity and transmission pathways of infectious diseases such as Ebola is key to curtailing an outbreak. Public health education when available can limit the intensity and duration of an Ebola outbreak in any community if there is compliance. It is important that all population groups be informed about the methods in which Ebola is transmitted to control the disease when there is an outbreak. In this paper, we study the impact of public health education that leads to behavioral changes on the dynamics of Ebola spread. The model is formulated as a system of ordinary differential equations and incorporates direct transmission from infectious, hospitalized, and deceased individuals with Ebola. We establish the existence of a disease free equilibrium and an endemic equilibrium, and investigate them for local and global stability. Model predictions show that a more informed community results in fewer cases, and thus limits the impact of an Ebola outbreak. Further, the model also predicts subsequent outbreak waves within a community in the absence of complete eradication. Lastly, the model successfully captures the dynamics of the 2014–2016 West Africa Ebola outbreak and the 2018–2020 Democratic Republic of Congo Ebola outbreak.

Ethical and sociocultural challenges in managing dead bodies during epidemics and natural disasters

Background

Catastrophic natural disasters and epidemics claim thousands of lives and have severe and lasting consequences, accompanied by human suffering. The Ebola epidemic of 2014–2016 and the current COVID-19 pandemic have revealed some of the practical and ethical complexities relating to the management of dead bodies. While frontline staff are tasked with saving lives, managing the bodies of those who die remains an under-resourced and overlooked issue, with numerous ethical and practical problems globally.

Methods

This scoping review of literature examines the management of dead bodies during epidemics and natural disasters. 82 articles were reviewed, of which only a small number were empirical studies focusing on ethical or sociocultural issues that emerge in the management of dead bodies.

Results

We have identified a wide range of ethical and sociocultural challenges, such as ensuring dignity for the deceased while protecting the living, honouring the cultural and religious rituals surrounding death, alleviating the suffering that accompanies grieving for the survivors and mitigating inequalities of resource allocation. It was revealed that several ethical and sociocultural issues arise at all stages of body management: notification, retrieving, identification, storage and burial of dead bodies.

Conclusion

While practical issues with managing dead bodies have been discussed in the global health literature and the ethical and sociocultural facets of handling the dead have been recognised, they are nonetheless not given adequate attention. Further research is needed to ensure care for the dead in epidemics and that natural disasters are informed by ethical best practice.

Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices.

Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device.

Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001.

Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.

The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models

Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March-June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ([Formula: see text]) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ([Formula: see text]) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.

Measuring the scientific effectiveness of contact tracing: Evidence from a natural experiment

Contact tracing constitutes the backbone of nonpharmaceutical public interventions against COVID-19, as it did with previous pandemics. Experts argue that its importance rises again as vaccination rates increase and the spread of COVID-19 slows, which makes tracing of individual cases possible. However, because randomized experiments on contact tracing are infeasible, causal evidence about its effectiveness is missing. This shortage of evidence is alarming as governments around the world invest in large-scale contact tracing systems, frequently facing a lack of cooperation from the population. Exploiting a large-scale natural experiment, we provide evidence that contact tracing may be even more effective than indicated by previous correlational research. Our findings inform current and future public health responses to the spread of infectious diseases.

Contact tracing has for decades been a cornerstone of the public health approach to epidemics, including Ebola, severe acute respiratory syndrome, and now COVID-19. It has not yet been possible, however, to causally assess the method’s effectiveness using a randomized controlled trial of the sort familiar throughout other areas of science. This study provides evidence that comes close to that ideal. It exploits a large-scale natural experiment that occurred by accident in England in late September 2020. Because of a coding error involving spreadsheet data used by the health authorities, a total of 15,841 COVID-19 cases (around 20% of all cases) failed to have timely contact tracing. By chance, some areas of England were much more severely affected than others. This study finds that the random breakdown of contact tracing led to more illness and death. Conservative causal estimates imply that, relative to cases that were initially missed by the contact tracing system, cases subject to proper contact tracing were associated with a reduction in subsequent new infections of 63% and a reduction insubsequent COVID-19–related deaths of 66% across the 6 wk following the data glitch.

Development and implementation of the Ebola Exposure Window Calculator: A tool for Ebola virus disease outbreak field investigations

During an Ebola virus disease (EVD) outbreak, calculating the exposure window of a confirmed case can assist field investigators in identifying the source of infection and establishing chains of transmission. However, field investigators often have difficulty calculating this window. We developed a bilingual (English/French), smartphone-based field application to assist field investigators in determining the exposure window of an EVD case. The calculator only requires the reported date of symptoms onset and the type of symptoms present at onset or the date of death. Prior to the release of this application, there was no similar electronic capability to enable consistent calculation of EVD exposure windows for field investigators. The Democratic Republic of the Congo Ministry of Health endorsed the application and incorporated it into trainings for field staff. Available for Apple and Android devices, the calculator continues to be downloaded even as the eastern DRC outbreak resolved. We rapidly developed and implemented a smartphone application to estimate the exposure window for EVD cases in an outbreak setting

Acute Respiratory Illnesses in Children in the SARS-CoV-2 Pandemic: Prospective Multicenter Study

OBJECTIVES

Nonpharmaceutical interventions against coronavirus disease 2019 likely have a role in decreasing viral acute respiratory illnesses (ARIs). We aimed to assess the frequency of respiratory syncytial virus (RSV) and influenza ARIs before and during the coronavirus disease 2019 pandemic.

METHODS

This study was a prospective, multicenter, population-based ARI surveillance, including children seen in the emergency departments and inpatient settings in 7 US cities for ARI. Respiratory samples were collected and evaluated by molecular testing. Generalized linear mixed-effects models were used to evaluate the association between community mitigation and number of eligible and proportion of RSV and influenza cases.

RESULTS

Overall, 45 759 children were eligible; 25 415 were enrolled and tested; 25% and 14% were RSV-positive and influenza-positive, respectively. In 2020, we noted a decrease in eligible and enrolled ARI subjects after community mitigation measures were introduced, with no RSV or influenza detection from April 5, 2020, to April 30, 2020. Compared with 2016-2019, there was an average of 10.6 fewer eligible ARI cases per week per site and 63.9% and 45.8% lower odds of patients testing positive for RSV and influenza, respectively, during the 2020 community mitigation period. In all sites except Seattle, the proportions of positive tests for RSV and influenza in the 2020 community mitigation period were lower than predicted.

CONCLUSIONS

Between March and April 2020, rapid declines in ARI cases and the proportions of RSV and influenza in children were consistently noted across 7 US cities, which could be attributable to community mitigation measures against severe acute respiratory syndrome coronavirus 2.

A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

Covid-19 Community Mitigation Status at Selected Districts of Southwest Ethiopia: A Mixed Design Survey

BACKGROUND

The spread of covid-19 was alarmingly continued in Ethiopia. This survey assessed the status of community mitigations to fight the pandemic. The ongoing forward effort by local task forces can be assessed to note the achievements.

METHODS

A mixed design using quantitative and qualitative triangulations used. Data was collected through interviewer administration using a structured W.H.O tool. The univariate and bivariate analysis employed to analyze descriptive statistics. The logistic regression model was applied to control confounders and determine potent predictors.

OBJECTIVE

This study assessed community mitigation status on covid-19 pandemic at four selected districts of southwest Ethiopia: a mixed design survey.

RESULTS

From the total of 624 participants interviewed, nearly half reported good mitigations toward fighting the covid-19 epidemic. This study suggested that nearly half (54.2%) of the participants had good knowledge about the newly emerged epidemic symptoms. Three out of five participants had good Knowledge of preventive practices (63.1%). Nearly four out of five (72.6%) participants were knowledgeable about 14 days incubation period. The odds of having good mitigation to prevent covid-19 among the participants who had single marital status were 55% lower than those married union (AOR=0.45, 95% CI: 0.24, 0.86). The odds of having good mitigation to prevent covid-19 among the participants (good knowledge symptoms) were 3.4 times higher than those with poor knowledge (AOR= 3.39, 95% CI: 2.19, 5.23).

CONCLUSIONS AND RECOMMENDATIONS

Participants' mitigation status to fight covid-19 was promising. Handwashing with soap and water, disinfecting surfaces, and covering mouth or nose while coughing were mitigated practices by the vast majority. Home staying was the least mitigated practice. Participants' demographic status, knowledge of the epidemic symptoms, and knowledge of preventive measures were potent predictors of mitigations to fight covid-19. HID services should be extended to the rural population through HCWs and task forces.

Statistical Estimation of the Reproductive Number From Case Notification Data

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.

Infectious diseases and social distancing in nature

Spread of contagious pathogens critically depends on the number and types of contacts between infectious and susceptible hosts. Changes in social behavior by susceptible, exposed, or sick individuals thus have far-reaching downstream consequences for infectious disease spread. Although "social distancing" is now an all too familiar strategy for managing COVID-19, nonhuman animals also exhibit pathogen-induced changes in social interactions. Here, we synthesize the effects of infectious pathogens on social interactions in animals (including humans), review what is known about underlying mechanisms, and consider implications for evolution and epidemiology.

The value of infectious disease modeling and trend assessment: a public health perspective

INTRODUCTION

Disease outbreaks of acquired immunodeficiency syndrome, severe acute respiratory syndrome, pandemic H1N1, H7N9, H5N1, Ebola, Zika, Middle East respiratory syndrome, and recently COVID-19 have raised the attention of the public over the past half-century. Revealing the characteristics and epidemic trends are important parts of disease control. The biological scenarios including transmission characteristics can be constructed and translated into mathematical models, which can help to predict and gain a deeper understanding of diseases.

AREAS COVERED

This review discusses the models for infectious diseases and highlights their values in the field of public health. This information will be of interest to mathematicians and clinicians, and make a significant contribution toward the development of more specific and effective models. Literature searches were performed using the online database of PubMed (inception to August 2020).

EXPERT OPINION

Modeling could contribute to infectious disease control by means of predicting the scales of disease epidemics, indicating the characteristics of disease transmission, evaluating the effectiveness of interventions or policies, and warning or forecasting during the pre-outbreak of diseases. With the development of theories and the ability of calculations, infectious disease modeling would play a much more important role in disease prevention and control of public health.

Africa's COVID-19 Situation in Focus and Recent Happenings: A Mini Review

Given that COVID-19 (SARS-CoV-2) has crept into Africa, a major public health crisis or threat continues to linger on the continent. Many local governments and various stakeholders have stepped up efforts for early detection and management of COVID-19. This mini review highlights the current trend in Africa, history and general epidemiological information on the virus. Current ongoing efforts (e.g., improving testing capacity) and some effective ways (e.g., intensified surveillance, quick detection, contact tracing, isolation measures [e.g., quarantine], and social distancing) of preventing and managing COVID-19 in Africa are described. The review concludes by emphasizing the need for public health infrastructure development (e.g., laboratories, infectious disease centers, regional hospitals) and human capacity building for combating COVID-19 and potential future outbreaks. Additionally, regular public health educational campaigns are urgently required. Future epidemiological studies to ascertain case fatality and mortality trends across the continent for policy directions are necessary.

Face-Masking, an Acceptable Protective Measure against COVID-19 in Ugandan High-Risk Groups

Face-masking could reduce the risk of COVID-19 transmission. We assessed knowledge, attitudes, perceptions, and practices toward COVID-19 and face-mask use among 644 high-risk individuals in Kampala, Uganda. In data analysis, descriptive, bivariate, and multivariate logistic regression analyses with a 95% CI were considered. Adjusted odds ratios were used to determine the magnitude of associations. P-values < 0.05 were considered statistically significant. The majority, 99.7% and 87.3% of the participants, respectively, had heard about COVID-19 and believed that face-masks were protective against COVID-19, whereas 67.9% reported having received information on face-mask use. Food-market vendors and those with no formal education were 0.5 and 0.3 times less likely to have received information about face-mask use than hospital workers and those who had completed secondary school, respectively. Those who had received information on face-mask use were 2.9 and 1.8 times more likely to own face-masks and to perceive them as protective, respectively. Food-market vendors were 3.9 times more likely to reuse their face-masks than hospital workers. Our findings suggest that Ugandan high-risk groups have good knowledge, optimistic attitudes and perceptions, and relatively appropriate practices toward COVID-19.

Qatar, the Coronavirus, and Cordons Sanitaires: Migrant Workers and the Use of Public Health Measures to Define the Nation

This article looks at the use of public health strategies to define political membership in the nation. I examine the use of the cordon sanitaire to mitigate the novel coronavirus in Qatar. I argue that it acts primarily as a boundary to map out zones of political exclusion, splitting those who are entitled to protection from disease from those who are not. Through an analysis of the logic, application, and history of the cordon sanitaire in Qatar and elsewhere, I argue that it is only a more explicit example of the ways that governments have applied public health measures such that they apportion exposure to COVID-19, protecting some while mandating exposure for others. Exposure, or protection from it, has become a means to spatialize power and territorialize the national imaginary, separating full members from those who are excluded and reduced to their economic function.

High-Level Design and Rapid Implementation of a Clinical and Non-clinical Blockchain-Based Data Sharing Platform for COVID-19 Containment

As the world has moved toward rigorous containment measures due to the spread of a novel coronavirus, it is crucial to push the boundaries of clinical data obtaining methods using real-time capturing facilities. During this time of crisis, data-centric technologies that could provide authenticity and immediate access to patient data are essential. A blockchain-based digital health protocol for access to real-time data with user-centric data protection measures can achieve these aims. Immediate and secure access to biomedical data can provide credible insights and also help in discovering intelligence to expediate the development of effective therapeutics. It also aids in altering policies for restrictions by extracting key insights required for modeling studies. This paper delivers a high-level design of a blockchain-based clinical research data collection and health service intervention platform, where the users can exercise control of data. This application also provides a platform to deliver technology-based interventions which would assist in streamlining aid for vulnerable users to prevent the NHS from being overwhelmed. Further steps are also recommended to achieve a data compliant solution for rapid deployment, based on available resources, allowing a collaborative effort, which is extremely necessary at times of such crisis.

Methods for Rapid Mobility Estimation to Support Outbreak Response

When pressed for time, outbreak investigators often use homogeneous mixing models to model infectious diseases in data-poor regions. But recent outbreaks such as the 2014 Ebola outbreak in West Africa have shown the limitations of this approach in an era of increasing urbanization and connectivity. Both outbreak detection and predictive modeling depend on realistic estimates of human and disease mobility, but these data are difficult to acquire in a timely manner. This is especially true when dealing with an emerging outbreak in an under-resourced nation. Weighted travel networks with realistic estimates for population flows are often proprietary, expensive, or nonexistent. Here we propose a method for rapidly generating a mobility model from open-source data. As an example, we use road and river network data, along with population estimates, to construct a realistic model of human movement between health zones in the Democratic Republic of the Congo (DRC). Using these mobility data, we then fit an epidemic model to real-world surveillance data from the recent Ebola outbreak in the Nord Kivu region of the DRC to illustrate a potential use of the generated mobility estimation. In addition to providing a way for rapid risk estimation, this approach brings together novel techniques to merge diverse GIS datasets that can then be used to address issues that pertain to public health and global health security.

Disease modeling for public health: added value, challenges, and institutional constraints

Public health policymakers face increasingly complex questions and decisions and need to deal with an increasing quantity of data and information. For policy advisors to make use of scientific evidence and to assess available intervention options effectively and therefore indirectly for those deciding on and implementing public health policies, mathematical modeling has proven to be a useful tool. In some areas, the use of mathematical modeling for public health policy support has become standard practice at various levels of decision-making. To make use of this tool effectively within public health organizations, it is necessary to provide good infrastructure and ensure close collaboration between modelers and policymakers. Based on experience from a national public health institute, we discuss the strategic requirements for good modeling practice for public health. For modeling to be of maximal value for a public health institute, the organization and budgeting of mathematical modeling should be transparent, and a long-term strategy for how to position and develop mathematical modeling should be in place.

All Hands on Deck: A synchronized whole-of-world approach for COVID-19 mitigation

The COVID-19 pandemic can no longer be mitigated by a nationwide approach of individual nations alone. Given its scale and accelerating expansion, COVID-19 requires a coordinated and simultaneous Whole- of-World approach that galvanizes clear global leadership and solidarity from all governments of the world. Considering an 'all hands-on deck' concept, we present a comprehensive list of tools and entities responsible for enabling them, as well a conceptual framework to achieve the maximum impact. The list is drawn from pandemic mitigation tools developed in response to past outbreaks including influenza, coronaviruses, and Ebola, and includes tools to minimize transmission in various settings including person-to-person, crowd, funerals, travel, workplace, and events and gatherings including business, social and religious venues. Included are the roles of individuals, communities, government and other sectors such as school systems, health, institutions, and business. While individuals and communities have significant responsibilities to prevent person-to-person transmission, other entities can play a significant role to enable individuals and communities to make use of the tools. Historic and current data indicate the role of political will, whole-of-government approach, and the role of early introduction of mitigation measures. There is also an urgent need to further elucidate the immunologic mechanisms underlying the epidemiological characteristics such as the low disease burden among women, and the role of COVID-19 in inducing Kawasaki-like syndromes in children. Understanding the role of and development of anti-inflammatory strategies based on our understanding of pro-inflammatory cytokines (IL1, IL-6) is also critical. Similarly, the role of oxygen therapy as an anti-inflammatory strategy is evident and access to oxygen therapy should be prioritized to avoid the aggravation of COVID-19 infection. We highlight the need for global solidarity to share both mitigation commodities and infrastructure between countries. Given the global reach of COVID-19 and potential for repeat waves of outbreaks, we call on all countries and communities to act synergistically and emphasize the need for synchronized pan-global mitigation efforts to minimize everyone's risk, to maximize collaboration, and to commit to shared progress.

Estimating the protective effect of case isolation with transmission tree reconstruction during the Ebola outbreak in Nigeria, 2014

The mainstream interventions used during the 2014-2016 Ebola epidemic were contact tracing and case isolation. The Ebola outbreak in Nigeria that formed part of the 2014-2016 epidemic demonstrated the effectiveness of control interventions with a 100% hospitalization rate. Here, we aim to explicitly estimate the protective effect of case isolation, reconstructing the time events of onset of illness and hospitalization as well as the transmission network. We show that case isolation reduced the reproduction number and shortened the serial interval. Employing Bayesian inference with the Markov chain Monte Carlo method for parameter estimation and assuming that the reproduction number exponentially declines over time, the protective effect of case isolation was estimated to be 39.7% (95% credible interval: 2.4%-82.1%). The individual protective effect of case isolation was also estimated, showing that the effectiveness was dependent on the speed, i.e. the time from onset of illness to hospitalization.

Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

As coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyzed the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detected change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we could quantify the effect of interventions and incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.

Mobility restrictions for the control of epidemics: When do they work?

Background

Mobility restrictions—trade and travel bans, border closures and, in extreme cases, area quarantines or cordons sanitaires—are among the most widely used measures to control infectious diseases. Restrictions of this kind were important in the response to epidemics of SARS (2003), H1N1 influenza (2009), Ebola (2014) and, currently in the containment of the ongoing COVID-19 pandemic. However, they do not always work as expected.

Methods

To determine when mobility restrictions reduce the size of an epidemic, we use a model of disease transmission within and between economically heterogeneous locally connected communities. One community comprises a low-risk, low-density population with access to effective medical resources. The other comprises a high-risk, high-density population without access to effective medical resources.

Findings

Unrestricted mobility between the two risk communities increases the number of secondary cases in the low-risk community but reduces the overall epidemic size. By contrast, the imposition of a cordon sanitaire around the high-risk community reduces the number of secondary infections in the low-risk community but increases the overall epidemic size.

Interpretation

Mobility restrictions may not be an effective policy for controlling the spread of an infectious disease if it is assessed by the overall final epidemic size. Patterns of mobility established through the independent mobility and trade decisions of people in both communities may be sufficient to contain epidemics.

Community context for mechanisms of disease dilution: insights from linking epidemiology and plant-soil feedback theory

In many natural systems, diverse host communities can reduce disease risk, though less is known about the mechanisms driving this "dilution effect." We relate feedback theory, which focuses on pathogen-mediated coexistence, to mechanisms of dilution derived from epidemiological models, with the central goal of gaining insights into host-pathogen interactions in a community context. We first compare the origin, structure, and application of epidemiological and feedback models. We then explore the mechanisms of dilution, which are grounded in single-pathogen, single-host epidemiological models, from the perspective of feedback theory. We also draw on feedback theory to examine how coinfecting pathogens, and pathogens that vary along a host specialist-generalist continuum, apply to dilution theory. By identifying synergies among the feedback and epidemiological approaches, we reveal ways in which organisms occupying different trophic levels contribute to diversity-disease relationships. Additionally, using feedbacks to distinguish dilution in disease incidence from dilution in the net effect of disease on host fitness allows us to articulate conditions under which definitions of dilution may not align. After ascribing dilution mechanisms to macro- or microorganisms, we propose ways in which each contributes to diversity-disease and productivity-diversity relationships. Our analyses lead to predictions that can guide future research efforts.

Preface to theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'

This preface forms part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Mathematical Analysis of the Ross-Macdonald Model with Quarantine

People infected with malaria may receive less mosquito bites when they are treated in well-equipped hospitals or follow doctors’ advice for reducing exposure to mosquitoes at home. This quarantine-like intervention measure is especially feasible in countries and areas approaching malaria elimination. Motivated by mathematical models with quarantine for directly transmitted diseases, we develop a mosquito-borne disease model where imperfect quarantine is considered to mitigate the disease transmission from infected humans to susceptible mosquitoes. The basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_0$$\end{document}R0 is computed and the model equilibria and their stabilities are analyzed when the incidence rate is standard or bilinear. In particular, the model system may undergo a subcritical (backward) bifurcation at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_0=1$$\end{document}R0=1 when standard incidence is adopted, whereas the disease-free equilibrium is globally asymptotically stable as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_0\le 1$$\end{document}R0≤1 and the unique endemic equilibrium is locally asymptotically stable as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_0>1$$\end{document}R0>1 when the infection incidence is bilinear. Numerical simulations suggest that the quarantine strategy can play an important role in decreasing malaria transmission. The success of quarantine mainly relies on the reduction of bites on quarantined individuals.

Nontrivial resource requirement in the early stage for containment of epidemics

During epidemic control, containment of the disease is usually achieved through increasing a devoted resource to reduce the infectiousness. However, the impact of this resource expenditure has not been studied quantitatively. For disease spread, the recovery rate can be positively correlated with the average amount of resource devoted to infected individuals. By incorporating this relation we build a novel model and find that insufficient resource leads to an abrupt increase in the infected population size, which is in marked contrast with the continuous phase transitions believed previously. Counterintuitively, this abrupt phase transition is more pronounced in less contagious diseases. Furthermore, we find that even for a single infection source, the public resource needs to be available in a significant amount, which is proportional to the total population size, to ensure epidemic containment. Our findings provide a theoretical foundation for efficient epidemic containment strategies in the early stage.

Lessons learned from engaging communities for Ebola vaccine trials in Sierra Leone: reciprocity, relatability, relationships and respect (the four R's)

BACKGROUND

Building trust and engaging the community are important for biomedical trials. This was core to the set up and delivery of the EBOVAC-Salone and PREVAC Ebola vaccine trials in Sierra Leone during and following the 2014-2016 West African Ebola epidemic. Local community liaison teams (CLT) engaged with the community through public meetings, radio chat shows, and other activities, while a social science team (SST) assessed community members' and participants' perceptions and regularly updated the clinical team to adapt procedures to improve the acceptability and compliance of the trial. The objective of this study was to examine the community engagement (CE) program in these trials and to identify potential barriers and facilitators.

METHODS

Fifteen CLT and SST members participated in in-depth interviews and 23 community members attended three focus groups to discuss the Ebola vaccine trials and their experiences and perspectives of the CE activities.

RESULTS

A key aim of the CE program was to build trust between the community and the trial. Four main principles (the "four R's") evolved from the discussions with team members and the community that influenced this trust: reciprocity, relatability, relationships and respect. The CLT and SST ensured reciprocal communication between the trial team and the community. The CLT delivered key messages from the trial, whilst the SST completed ethnographic research in the field to uncover rumors and perceptions of the trial in the community. These ethnographic findings were shared with the CLT and addressed in targeted messaging to the community. Both the CLT and SST approached the communities in an egalitarian manner, by dressing modestly, speaking local dialects, and using relatable examples. Appreciation and understanding of the importance of interpersonal relationships and respect for the people, their customs, and traditions also played a large role in the CE program.

CONCLUSION

These findings provide an in-depth understanding of how interdisciplinary community liaison and social science teams can work with a clinical team to strengthen trust. The four R's suggest the ways in which trust relations are central to CE and confidence in vaccine trials, and could offer an approach to CE in vaccine trials.

Forecasting the 2014 West African Ebola Outbreak

In 2014–2015, a large Ebola outbreak afflicted Liberia, Guinea, and Sierra Leone. We performed a systematic review of 26 manuscripts, published between 2014 and April 2015, that forecasted the West African Ebola outbreak while it was occurring, and we derived implications for how results could be interpreted by policymakers. Forecasted case counts varied widely. An important determinant of forecast accuracy for case counts was how far into the future predictions were made. Generally, forecasts for less than 2 months into the future tended to be more accurate than those made for more than 10 weeks into the future. The exceptions were parsimonious statistical models in which the decay of the rate of spread of the pathogen among susceptible individuals was dealt with explicitly. The most important lessons for policymakers regarding future outbreaks, when using similar modeling results, are: 1) uncertainty of forecasts will be greater in the beginning of the outbreak; 2) when data are limited, forecasts produced by models designed to inform specific decisions should be used complementarily for robust decision-making (e.g., 2 statistical models produced the most reliable case-counts forecasts for the studied Ebola outbreak but did not enable understanding of interventions’ impact, whereas several compartmental models could estimate interventions’ impact but required unavailable data); and 3) timely collection of essential data is necessary for optimal model use.

Vaccination strategies to control Ebola epidemics in the context of variable household inaccessibility levels

Despite a very effective vaccine, active conflict and community distrust during the ongoing DRC Ebola epidemic are undermining control efforts, including a ring vaccination strategy that requires the prompt immunization of close contacts of infected individuals. However, in April 2019, it was reported 20% or more of close contacts cannot be reached or refuse vaccination, and it is predicted that the ring vaccination strategy would not be effective with such a high level of inaccessibility. The vaccination strategy is now incorporating a “third ring” community-level vaccination that targets members of communities even if they are not known contacts of Ebola cases. To assess the impact of vaccination strategies for controlling Ebola epidemics in the context of variable levels of community accessibility, we employed an individual-level stochastic transmission model that incorporates four sources of heterogeneity: a proportion of the population is inaccessible for contact tracing and vaccination due to lack of confidence in interventions or geographic inaccessibility, two levels of population mixing resembling household and community transmission, two types of vaccine doses with different time periods until immunity, and transmission rates that depend on spatial distance. Our results indicate that a ring vaccination strategy alone would not be effective for containing the epidemic in the context of significant delays to vaccinating contacts even for low levels of household inaccessibility and affirm the positive impact of a supplemental community vaccination strategy. Our key results are that as levels of inaccessibility increase, there is a qualitative change in the effectiveness of the vaccination strategy. For higher levels of vaccine access, the probability that the epidemic will end steadily increases over time, even if probabilities are lower than they would be otherwise with full community participation. For levels of vaccine access that are too low, however, the vaccination strategies are not expected to be successful in ending the epidemic even though they help lower incidence levels, which saves lives, and makes the epidemic easier to contain and reduces spread to other communities. This qualitative change occurs for both types of vaccination strategies: ring vaccination is effective for containing an outbreak until the levels of inaccessibility exceeds approximately 10% in the context of significant delays to vaccinating contacts, a combined ring and community vaccination strategy is effective until the levels of inaccessibility exceeds approximately 50%. More broadly, our results underscore the need to enhance community engagement to public health interventions in order to enhance the effectiveness of control interventions to ensure outbreak containment.

In the context of the ongoing Ebola epidemic in DRC, active conflict and community distrust are undermining control efforts, including vaccination strategies. In this paper, we employed an individual-level stochastic structured transmission model to assess the impact of vaccination strategies on epidemic control in the context of variable levels of household inaccessibility. We found that a ring vaccination strategy of close contacts would not be effective for containing the epidemic in the context of significant delays to vaccinating contacts even for low levels of household inaccessibility and evaluate the impact of a supplemental community vaccination strategy. For lower levels of inaccessibility, the probability of epidemic containment increases over time. For higher levels of inaccessibility, even the combined ring and community vaccination strategies are not expected to contain the epidemic even though they help lower incidence levels, which saves lives, makes the epidemic easier to contain and reduces spread to other communities. We found that ring vaccination is effective for containing an outbreak until the levels of inaccessibility exceeds approximately 10%, a combined ring and community vaccination strategy is effective until the levels of inaccessibility exceeds approximately 50%. Our findings underscore the need to enhance community engagement to public health interventions.