Economic burden of rabies and its impact in Bangladesh through a One Health approach (original) (raw)
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New Frontiers in Regional Science: Asian Perspectives, 2020
Modern levels of global travel have intensified the risk of new infectious diseases becoming pandemics. Particularly at risk are developing countries whose health systems may be less well equipped to detect quickly and respond effectively to the importation of new infectious diseases. This chapter examines what might have been the economic consequences if the 2014 West African Ebola epidemic had been imported to a small Asia-Pacific country. Hypothetical outbreaks in two countries were modelled. The post-importation estimations were carried out with two linked models: a stochastic disease transmission (SEIR) model and a quarterly version of the multi-country GTAP model, GTAP-Q. The SEIR model provided daily estimates of the number of persons in various disease states. For each intervention strategy the stochastic disease model was run 500 times to obtain the probability distribution of disease outcomes. Typical daily country outcomes for both controlled and uncontrolled outbreaks under five alternative intervention strategies were converted to quarterly disease-state results, which in turn were used in the estimation of GTAP-Q shocks to country-specific health costs and labour productivity during the outbreak, and permanent reductions in each country's population and labour force due to mortality. Estimated behavioural consequences
Assessing the International Spreading Risk Associated with the 2014 West African Ebola Outbreak
PLoS Currents, 2014
Background: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. The affected countries, Sierra Leone, Guinea, Liberia, and Nigeria, have been struggling to contain and to mitigate the outbreak. The ongoing rise in confirmed and suspected cases, 2615 as of 20 August 2014, is considered to increase the risk of international dissemination, especially because the epidemic is now affecting cities with major commercial airports.
A modified SEIR model for the spread of Ebola in Western Africa and metrics for resource allocation
Applied Mathematics and Computation
A modified, deterministic SEIR model is developed for the 2014 Ebola epidemic occurring in the West African nations of Guinea, Liberia, and Sierra Leone. The model describes the dynamical interaction of susceptible and infected populations, while accounting for the effects of hospitalization and the spread of disease through interactions with deceased, but infectious, individuals. Using data from the World Health Organization (WHO), parameters within the model are fit to recent estimates of infected and deceased cases from each nation. The model is then analyzed using these parameter values. Finally, several metrics are proposed to determine which of these nations is in greatest need of additional resources to combat the spread of infection. These include local and global sensitivity metrics of both the infected population and the basic reproduction number with respect to rates of hospitalization and proper burial.
2019
Public health practitioners require measures to evaluate how vulnerable populations are to diseases, especially for zoonoses (i.e. diseases transmitted from animals to humans) given their pandemic potential. These measures would be valuable to support strategic and operational decision making and allocation of resources. Although vulnerability is well defined for natural hazards, for public health threats the concept remains undetermined. Here, we develop new methodologies to: (i) quantify the impact of zoonotic diseases and the capacity of countries to cope with these diseases, and (ii) combine these two measures (impact and capacity) into one overall vulnerability indicator. The adaptive capacity is calculated from estimations of disease mortality, although the method can be adapted for diseases with no or low mortality but high morbidity. As an example, we focused on the vulnerability of Nigeria and Sierra Leone to Lassa Fever and Ebola. We develop a simple analytical form that c...
Bulletin of Mathematical Biology, 2015
Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the international health authorities due to important outbreaks in some Western African countries and possible spread to other continents, which has already occurred in the USA and Spain. Regarding the emergency of this situation, there is a need of development of decision tools to assist the authorities to focus their efforts in important factors to eradicate Ebola. In particular, mathematical modelling can help to predict the possible evolution of the Ebola outbreaks and to give some recommendations about surveillance. In this work, we propose a novel spatial and temporal model, called Be-CoDiS (Between-COuntries Disease Spread), to study the evolution of human diseases between countries. The goal is to simulate the spread of a particular disease and identify risk zones worldwide. The main interesting characteristics of Be-CoDiS are the consideration of the migratory flux between countries and control measure effects and the use of time dependent coefficients adapted to each country. First, we focus on the mathematical formulation of each component of the model. Next, in order to validate our approach, we consider various numerical experiments regarding the 2014 Ebola epidemic. In particular, we study the ability of the model in predicting the EVD evolution at 30 days and until the end of the epidemic. The results are compared to real data and other models outputs found in the literature. Finally, a brief parameter sensitivity analysis is done.
Understanding the dynamics of Ebola epidemics
Epidemiology and Infection, 2007
SUMMARYEbola is a highly lethal virus, which has caused at least 14 confirmed outbreaks in Africa between 1976 and 2006. Using data from two epidemics [in Democratic Republic of Congo (DRC) in 1995 and in Uganda in 2000], we built a mathematical model for the spread of Ebola haemorrhagic fever epidemics taking into account transmission in different epidemiological settings. We estimated the basic reproduction number (R0) to be 2·7 (95% CI 1·9–2·8) for the 1995 epidemic in DRC, and 2·7 (95% CI 2·5–4·1) for the 2000 epidemic in Uganda. For each epidemic, we quantified transmission in different settings (illness in the community, hospitalization, and traditional burial) and simulated various epidemic scenarios to explore the impact of control interventions on a potential epidemic. A key parameter was the rapid institution of control measures. For both epidemic profiles identified, increasing hospitalization rate reduced the predicted epidemic size.
Mathematical modeling of Ebola epidemics with public health intervention
The 2014/2015 Ebola epidemic in West Africa is the leading ever recorded, and identifying the integrated and applicable dynamics of public health intervention is a key concern, both for current and future epidemics. Moreover, as transmissibility and mortality are supposed to increase as symptoms progress, intervention approaches may depend on individual’s stage of infection. To inspect these issues, we develop SEIIsR mathematical model that study the control mechanism and spread of Ebola epidemic by reducing Is compartment. This work is intensively analysis the sensitive model parameters, disease free equilibrium point locally and globally asymptotically stability, existence of endemic equilibrium point, simulation study and data fitting. A variety of intervention measures exist to prevent and control epidemic diseases. In this study Isolation and contact tracing are proposed. Isolation is an important control strategy for containing Ebola epidemics. The analysis showed that if the most essential epidemiological parameter so called basic reproductive number R0 < 1 then disease free equilibrium point is stable whereas endemic equilibrium point is exist and stable if R0 > 1. The result indicated that early case detection followed by strict isolation could control Ebola outbreak. Tracing close contacts of cases and contacts of exposed health care workers additionally reduces the number of new infected cases. The study emphasizes the significance of early identification and isolation of Ebola cases to reduce the number of people getting infected. According to the numerical solution early identification or Isolated from Exposed is more significance than infected compartment. The best investigation of this study acknowledged that, it is possible to control the outbreak with short period of time by using effective contact tracing and isolation even if the value of R0 > 1. In SEIIsR model the best fit of cumulative infected case in West Africa is computed to follow simulated curve with R0 = 1.3. Further, the study supports the cumulative death cases due to Ebola epidemic is 63% of the infected individuals. Without intervention Ebola epidemic spread is going on where R0 > 1 whereas it is die out where R0 < 1. If we isolate more than 30% of exposed and infected individuals it is possible to reduce and vanish the epidemics spread.
Simulating endogenous dynamics of intervention-capacity deployment: Ebola outbreak in Liberia
International Journal of Systems Science: Operations & Logistics, 2016
During the first months, the 2014 outbreak of the Ebola virus (EBOV) in West Africa was characterised by inadequate intervention capacities. In this paper, we investigate (1) the influence of limited but dynamic intervention capacities and their effect on the effective reproduction number, and (2) the effects of proactive versus reactive intervention approaches. We use a transmission model extended with dynamical intervention capacities. Taking into account a bandwidth for potential over-and under-reporting in reported Ebola virus disease cases, the model is used to generate ensembles of plausible scenarios. Next, it is used for testing the effectiveness of more proactive approaches in extending intervention capacities across these scenarios. We show that reactive approaches in extending intervention capacities can lead to continued under-capacity, and, consequently, to an increase of the effective reproduction number and to accelerated EBOV transmission. Proactive approaches, which take deployment delays, doubling times of diseases, and potential under-reporting of the number of cases into account, help in limiting the total number of cases and deaths if the effective reproduction number in isolation is lower than the effective reproduction number outside of isolation. If the effective reproduction number in isolation is higher, proactive intervention policies still outperform reactive intervention policies.
A multi-source global-local model for epidemic management
PLoS ONE, 2022
The Effective Reproduction Number Rt provides essential information for the management of an epidemic/pandemic. Projecting Rt into the future could further assist in the management process. This article proposes a methodology based on exposure scenarios to perform such a procedure. The method utilizes a compartmental model and its adequate parametrization; a way to determine suitable parameters for this model in México’s case is detailed. In conjunction with the compartmental model, the projection of Rt permits estimating unobserved variables, such as the size of the asymptomatic population, and projecting into the future other relevant variables, like the active hospitalizations, using scenarios. The uses of the proposed methodologies are exemplified by analyzing the pandemic in a Mexican state; the main quantities derived from the compartmental model, such as the active and total cases, are included in the analysis. This article also presents a national summary based on the method...
Modeling of the Deaths Due to Ebola Virus Disease Outbreak in Western Africa
International Journal of Statistics in Medical Research, 2015
Problem: The recent 2014 Ebola virus outbreak in Western Africa is the worst in history. It is imperative that appropriate statistical and mathematical models are used to identify risk factors and to monitor the development and spread of the disease. Method: Deaths data due to Ebola virus disease (EVD) in Guinea, Liberia, and Sierra Leone from October 10, 2014 to March 24, 2015 were collected via Situation Reports published by the World Health Organization [1]. Conditional autoregressive (CAR) models were applied to account for the spatial dependency in the countries along with the temporal dimension of the disease. Bayesian change-point models were used to identify key changes in growth and drop time points in the spatial distribution of deaths due to EVD within each country. Country-specific Poisson and negative binomial mixed models of covariate effects were applied to understand the between-country variability in deaths due to EVD. Results: Both CAR models and generalized linear mixed models identified statistically significant covariate effects; however, the CAR models depended on the interval of data analyzed, whereas the mixed models depended on the underlying distribution assumed. Bayesian change-point models identified one significant change-point in the distribution of deaths due to EVD within each country. Practical Application: CAR models, Bayesian change-point models, and generalized linear mixed models demonstrate useful techniques in modeling the incidence of deaths due to EVD.