Marcelo Ferreira da Costa Gomes

Assessing the International Spreading Risk Associated with the 2014 West African Ebola Outbreak

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. Method: We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 6 July to the date of 9 August 2014. The estimates obtained were used to generate a 3-month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in West Africa and the probability of international spread if the containment measures are not successful at curtailing the outbreak. Results: We model the short-term growth rate of the disease in the affected West African countries and estimate the basic reproductive number to be in the range 1.5 − 2.0 (interval at the 1/10 relative likelihood). We simulated the international spreading of the outbreak and provide the estimate for the probability of Ebola virus disease case importation in countries across the world. Results indicate that the short-term (3 and 6 weeks) probability of international spread outside the African region is small, but not negligible. The extension of the outbreak is more likely occurring in African countries, increasing the risk of international dissemination on a longer time scale.

Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis

BACKGROUND The 2014 epidemic of Ebola virus disease in parts of west Africa defines an unprecedented health threat. We developed a model of Ebola virus transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions. METHODS We modelled the movements of individuals, including patients not infected with Ebola virus, seeking assistance in health-care facilities, the movements of individuals taking care of patients infected with Ebola virus not admitted to hospital, and the attendance of funerals. Individuals were grouped into randomly assigned households (size based on Demographic Health Survey data) that were geographically placed to match population density estimates on a grid of 3157 cells covering the country. The spatial agent-based model was calibrated with a Markov chain Monte Carlo approach. The model was used to estimate Ebola virus transmission parameters and investigate the effectiveness of interventions such as availability of Ebola treatment units, safe burials procedures, and household protection kits. FINDINGS Up to Aug 16, 2014, we estimated that 38·3% of infections (95% CI 17·4-76·4) were acquired in hospitals, 30·7% (14·1-46·4) in households, and 8·6% (3·2-11·8) while participating in funerals. We noted that the movement and mixing, in hospitals at the early stage of the epidemic, of patients infected with Ebola virus and those not infected was a sufficient driver of the reported pattern of spatial spread. The subsequent decrease of incidence at country and county level is attributable to the increasing availability of Ebola treatment units (which in turn contributed to drastically decreased hospital transmission), safe burials, and distribution of household protection kits. INTERPRETATION The model allows assessment of intervention options and the understanding of their role in the decrease in incidence reported since Sept 7, 2014. High-quality data (eg, to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions) are needed to reduce uncertainty in model estimates. FUNDING US Defense Threat Reduction Agency, US National Institutes of Health.

Assessing the impact of travel restrictions on international spread of the 2014 West African Ebola epidemic.

The quick spread of an Ebola outbreak in West Africa has led a number of countries and airline companies to issue travel bans to the affected areas. Considering data up to 31 Aug 2014, we assess the impact of the resulting traffic reductions with detailed numerical simulations of the international spread of the epidemic. Traffic reductions are shown to delay by only a few weeks the risk that the outbreak extends to new countries.

Zika in Rio de Janeiro: Assessment of basic reproductive number and its comparison with dengue

Zika virus infection was declared a public health emergency of international concern in February 2016 in response to the outbreak in Brazil and its suspected link with congenital anomalies. In this study we use notification data and disease natural history parameters to estimate the basic reproduction number (R0) of Zika in Rio de Janeiro, Brazil. We also obtain estimates of R0 of dengue from time series of dengue cases in the outbreaks registered in 2002 and 2012 in the city, when DENV-3 and DENV-4 serotypes respectively, had just emerged. Our estimates of the basic reproduction number for Zika in Rio de Janeiro based on surveillance notifications (R0 = 2.33, 95% CI: 1.97 − 2.97) were higher than those obtained for dengue in the city (year 2002: R0 = 1.70 [1.50 − 2.02]; year 2012: Ro = 1.25 [1.18 − 1.36]). Given the role of Aedes aegypti as vector of both the Zika and dengue viruses, we also derive Ro of Zika as a function of both dengue reproduction number and entomological and epidemiological parameters for dengue and Zika. Using the dengue outbreaks from previous years allowed us to estimate the potential R0 of Zika. Our estimates were closely in agreement with our first Zika’s R0 estimation from notification data. Hence, these results validate deriving the potential risk of Zika transmission in areas with recurring dengue outbreaks. Whether transmission routes other than vector-based can sustain a Zika epidemic still deserves attention, but our results suggest that the Zika outbreak in Rio de Janeiro emerged due to population susceptibility and ubiquitous presence of Ae. aegypti.

Oscillations in SIRS model with distributed delays

AbstractThe ubiquity of oscillations in epidemics presents a long standing challenge for the formulation of epidemic models. Whether they are external and seasonally driven, or arise from the intrinsic dynamics is an open problem. It is known that fixed time delays destabilize the steady state solution of the standard SIRS model, giving rise to stable oscillations for certain parameters values. In this contribution, starting from the classical SIRS model, we make a general treatment of the recovery and loss of immunity terms. We present oscillation diagrams (amplitude and period) in terms of the parameters of the model, showing how oscillations can be destabilized by the shape of the distributions of the two characteristic (infectious and immune) times. The formulation is made in terms of delay equations which are both numerically integrated and linearized. Results from simulations are included showing where they support the linear analysis and explaining why not where they do not. Considerations and comparison with real diseases are presented along.

Zika in Rio de Janeiro: assessment of basic reproduction number and comparison with dengue outbreaks

Zika virus infection was declared a public health emergency of international concern in February 2016 in response to the outbreak in Brazil and its suspected link with congenital anomalies. In this study, we use notification data and disease natural history parameters to estimate the basic reproduction number (R 0) of Zika in Rio de Janeiro, Brazil. We also obtain estimates of R 0 of dengue from time series of dengue cases in the outbreaks registered in 2002 and 2012 in the city, when DENV-3 and DENV-4 serotypes, respectively, had just emerged. Our estimates of the basic reproduction number for Zika in Rio de Janeiro based on surveillance notifications (R 0 = 2·33, 95% CI: 1·97-2·97) were higher than those obtained for dengue in the city (year 2002: R 0 = 1·70 [1·50-2·02]; year 2012: R 0 = 1·25 [1·18-1·36]). Given the role of Aedes aegypti as vector of both the Zika and dengue viruses, we also derive R 0 of Zika as a function of both dengue reproduction number and entomological and epidemiological parameters for dengue and Zika. Using the dengue outbreaks from previous years allowed us to estimate the potential R 0 of Zika. Our estimates were closely in agreement with our first Zikas R 0 estimation from notification data. Hence, these results validate deriving the potential risk of Zika transmission in areas with recurring dengue outbreaks. Whether transmission routes other than vector-based can sustain a Zika epidemic still deserves attention, but our results suggest that the Zika outbreak in Rio de Janeiro emerged due to population susceptibility and ubiquitous presence of Ae. aegypti.

Info Dengue: a nowcasting system for the surveillance of dengue fever transmission

This study describes the development of an integrated dengue alert system (InfoDengue), operating initially in the city of Rio de Janeiro, Brazil. It is a project developed as a partnership between academia and the municipal health secretariat. At the beginning of each epidemiological week, the system captures climate time series, dengue case reporting and activity on a social network. After data pre-processing, including a probabilistic correction of case notification delay, and calculation of dengues effective reproductive number, indicators of dengue transmission are coded into four dengue situation levels, for each of the citys ten health districts. A risk map is generated to inform the public about the weeks level of attention and the evolution of the disease incidence and suggest actions. A report is also sent automatically to the municipalitys situation room, containing a detailed presentation of the data and alert levels by health district. The preliminary analysis of InfoDengue in Rio de Janeiro, using historical series from 2011 to 2014 and prospective data from January to December 2015, indicates good degree of confidence and accuracy. The successful experience in the city of Rio de Janeiro is a motivating argument for the expansion of InfoDengue to other cities. After a year in production, InfoDengue has become a unique source of carefully curated data for epidemiological studies, combining epidemological and environmental variables in unprecedented spatial and temporal resolutions. Ethical committee approval: 26910214.7.0000.5240

Real-Time Assessment of the International Spreading Risk Associated with the 2014 West African Ebola Outbreak

The 2014 West African Ebola Outbreak is the largest Ebola virus disease (EVD) epidemic ever recorded, not only in number of cases but also in geographical extent. Unlike previous EVD outbreaks, the large number of cases observed in major cities with international airports raised the concern about the possibility of exportation of the infection in countries around the world. Starting in July 2014, we used the Global Epidemic and Mobility model to provide a real-time assessment of the potential international spread of the EVD epidemic. We modeled the unfolding of the outbreak in the most affected countries, considered different scenarios reflecting changes in the disease dynamic, and provided estimates for the probability of observing imported cases around the world for 220 countries. The model went through successive calibrations as more surveillance data were available, providing projections extending from a few weeks to several months. The results show that along the entire course of the epidemic the probability of observing cases outside of Africa was small, but not negligible, from September to November 2014. The inflection point of the epidemic occurred in late September and early October 2014 with a consistent longitudinal decrease in new cases, thus averting the status quo epidemic growth that could have seen hundreds of exported cases at the global scale in the following months.

Promiscuity and the evolution of sexual transmitted diseases

We study the relation between different social behaviors and the onset of epidemics in a model for the dynamics of sexual transmitted diseases. The model considers the society as a system of individual sexuated agents that can be organized in couples and interact with each other. The different social behaviors are incorporated assigning what we call a promiscuity value to each individual agent. The individual promiscuity is taken from a distribution and represents the daily probability of going out to look for a sexual partner, abandoning its eventual mate. In terms of this parameter we find a threshold for the epidemic which is much lower than the classical SIR model prediction, i.e., R0 (basic reproductive number)=1. Different forms for the distribution of the population promiscuity are considered showing that the threshold is weakly sensitive to them. We study the homosexual and the heterosexual case as well.

Spatio-temporal spread of the Ebola 2014 outbreak in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis

BACKGROUND The 2014 epidemic of Ebola virus disease in parts of west Africa defines an unprecedented health threat. We developed a model of Ebola virus transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions. METHODS We modelled the movements of individuals, including patients not infected with Ebola virus, seeking assistance in health-care facilities, the movements of individuals taking care of patients infected with Ebola virus not admitted to hospital, and the attendance of funerals. Individuals were grouped into randomly assigned households (size based on Demographic Health Survey data) that were geographically placed to match population density estimates on a grid of 3157 cells covering the country. The spatial agent-based model was calibrated with a Markov chain Monte Carlo approach. The model was used to estimate Ebola virus transmission parameters and investigate the effectiveness of interventions such as availability of Ebola treatment units, safe burials procedures, and household protection kits. FINDINGS Up to Aug 16, 2014, we estimated that 38·3% of infections (95% CI 17·4-76·4) were acquired in hospitals, 30·7% (14·1-46·4) in households, and 8·6% (3·2-11·8) while participating in funerals. We noted that the movement and mixing, in hospitals at the early stage of the epidemic, of patients infected with Ebola virus and those not infected was a sufficient driver of the reported pattern of spatial spread. The subsequent decrease of incidence at country and county level is attributable to the increasing availability of Ebola treatment units (which in turn contributed to drastically decreased hospital transmission), safe burials, and distribution of household protection kits. INTERPRETATION The model allows assessment of intervention options and the understanding of their role in the decrease in incidence reported since Sept 7, 2014. High-quality data (eg, to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions) are needed to reduce uncertainty in model estimates. FUNDING US Defense Threat Reduction Agency, US National Institutes of Health.