Probability Of Outbreak Research Articles

Revealing two dynamic dengue epidemic clusters in Thailand

BackgroundThailand is home to around 69 million individuals. Dengue is hyper-endemic and all 4 serotypes are in active circulation in the country. Dengue outbreaks occur almost annually within Thailand in at least one province but the spatio-temporal and environmental interface of these outbreaks has not been studied.MethodsWe develop Bayesian regime switching (BRS) models to characterize outbreaks, their persistence and infer their likelihood of occurrence across time for each administrative province where dengue case counts are collected. BRS was compared against two other classification tools and their agreement is assessed. We further examine how these spatio-temporal clusters of outbreak clusters arise by comparing reported dengue case counts, urban population, urban land cover, climate and flight volumes on the province level.ResultsTwo dynamic dengue epidemic clusters were found nationally. One cluster consists of 47 provinces and is highly outbreak prone. Provinces with a large number of case counts, urban population, urban land cover and incoming flight passengers are associated to the epidemic prone cluster of dengue. Climate has an effect on determining the probability of outbreaks over time within provinces, but have less influence on whether provinces belong to the epidemic prone cluster. BRS found high agreement with other classification tools.ConclusionsImportation and urbanization drives the risk of outbreaks across regions strongly. In provinces estimated to have high epidemic persistence, more resource allocation to vector control should be applied to those localities as heightened transmission counts are likely to occur over a longer period of time. Clustering of epidemic and non-epidemic prone areas also highlights the need for prioritization of resource allocation for disease mitigation over provinces in Thailand.

The effect of demographic and environmental variability on disease outbreak for a dengue model with a seasonally varying vector population

Seasonal changes in temperature, humidity, and rainfall affect vector survival and emergence of mosquitoes and thus impact the dynamics of vector-borne disease outbreaks. Recent studies of deterministic and stochastic epidemic models with periodic environments have shown that the average basic reproduction number is not sufficient to predict an outbreak. We extend these studies to time-nonhomogeneous stochastic dengue models with demographic variability wherein the adult vectors emerge from the larval stage vary periodically. The combined effects of variability and periodicity provide a better understanding of the risk of dengue outbreaks. A multitype branching process approximation of the stochastic dengue model near the disease-free periodic solution is used to calculate the probability of a disease outbreak. The approximation follows from the solution of a system of differential equations derived from the backward Kolmogorov differential equation. This approximation shows that the risk of a disease outbreak is also periodic and depends on the particular time and the number of the initial infected individuals. Numerical examples are explored to demonstrate that the estimates of the probability of an outbreak from that of branching process approximations agree well with that of the continuous-time Markov chain. In addition, we propose a simple stochastic model to account for the effects of environmental variability on the emergence of adult vectors from the larval stage.

Disease Emergence in Multi-Patch Stochastic Epidemic Models with Demographic and Seasonal Variability.

Factors such as seasonality and spatial connectivity affect the spread of an infectious disease. Accounting for these factors in infectious disease models provides useful information on the times and locations of greatest risk for disease outbreaks. In this investigation, stochastic multi-patch epidemic models are formulated with seasonal and demographic variability. The stochastic models are used to investigate the probability of a disease outbreak when infected individuals are introduced into one or more of the patches. Seasonal variation is included through periodic transmission and dispersal rates. Multi-type branching process approximation and application of the backward Kolmogorov differential equation lead to an estimate for the probability of a disease outbreak. This estimate is also periodic and depends on the time, the location, and the number of initial infected individuals introduced into the patch system as well as the magnitude of the transmission and dispersal rates and the connectivity between patches. Examples are given for seasonal transmission and dispersal in two and three patches.

Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine

Predicting the probability of CORONA virus outbreak has been studied in recent days, but the published literature seldom contains multiple model comparisons or predictive analysis of uncertainty. Time series parameters are the core factors influencing infectious diseases such as severe acute respiratory syndrome (SARS) and influenza. As a global pandemic is imminent, the prediction of real-time transmission of COVID-19 is crucial. The objective of this paper is to produce a real-time forecasts using the SVM model. The purpose of this study is to investigate the Corona Virus Disease 2019 (COVID-19) prediction of confirmed, deceased and recovered cases. This prediction will help to plan resources, determine government policy, and provide survivors with immunity passports, and use the same plasma for care. In this analysis, data including attributes such as location wise confirmed, deceased, recovered COVID-19, longitude and latitude were collected from January 22, 2020 to April 25, 2020 worldwide. Support Vector Machine was used to explore the impact on identification, deceased, and recovery.

Assessing the Risk of Occurrence of Bluetongue in Senegal.

Bluetongue is a non-contagious viral disease affecting small ruminants and cattle that can cause severe economic losses in the livestock sector. The virus is transmitted by certain species of the genus Culicoides and consequently, understanding their distribution is essential to enable the identification of high-risk transmission areas. In this work we use bioclimatic and environmental variables to predict vector abundance, and estimate spatial variations in the basic reproductive ratio . The resulting estimates were combined with livestock mobility and serological data to assess the risk of Bluetongue outbreaks in Senegal. The results show an increasing abundance of C. imicola, C. oxystoma, C. enderleini, and C. miombo from north to south. < 1 for most areas of Senegal, whilst southern (Casamance) and southeastern (Kedougou and part of Tambacounda) agro-pastoral areas have the highest risk of outbreak ( = 2.7 and 2.9, respectively). The next higher risk areas are in the Senegal River Valley ( = 1.07), and the Atlantic coast zones. Seroprevalence rates, shown by cELISA, weren’t positively correlated with outbreak probability. Future works should include follow-up studies of competent vector abundancies and serological surveys based on the results of the risk analysis conducted here to optimize the national epidemiological surveillance system.

Epidemiological Impact of SARS-CoV-2 Vaccination: Mathematical Modeling Analyses.

This study aims to inform SARS-CoV-2 vaccine development/licensure/decision-making/implementation, using mathematical modeling, by determining key preferred vaccine product characteristics and associated population-level impacts of a vaccine eliciting long-term protection. A prophylactic vaccine with efficacy against acquisition (VES) ≥70% can eliminate the infection. A vaccine with VES <70% may still control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, if it is supplemented with <20% reduction in contact rate, or if it is complemented with herd-immunity. At VES of 50%, the number of vaccinated persons needed to avert one infection is 2.4, and the number is 25.5 to avert one severe disease case, 33.2 to avert one critical disease case, and 65.1 to avert one death. The probability of a major outbreak is zero at VES ≥70% regardless of the number of virus introductions. However, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact. In addition to the reduction in infection acquisition, developers should assess the natural history and disease progression outcomes when evaluating vaccine impact.

Increasing Probability of Outbreak of Dengue in Asian Metropolises Under Global Warming of 1·5°C- 4°C

Background: Climate factors drive the spread of dengue. Global warming affects the mosquito habitat and the transmission of dengue, especially in densely populated urban areas. Methods: Transfer functions from climate to mosquito and from mosquito to disease are set up using the Generalized Additive Model (GAM) and Susceptible Infected Recovered Model (SIR), respectively, to project distribution patterns of mosquito density in Asia and changes of dengue incidence in Asian metropolises at global warming of 1·5°C, 2°C, 3°C and 4°C above the pre-industrial level, based on multi-model outputs from Coupled Model Inter-comparison Project Phase 6 (CMIP6). Findings: With high temperature, high humidity, and dense population, urban areas in South and Southeast Asia have favorable conditions for dengue fever transmission. In a warming world, the area suitable for dengue transmission is expected to expand in Asia. It is projected that Arabian Peninsula, north of South Asia, and north of East Asia will be under an emergent risk for dengue transmission. In the reference period (1995-2014), about 180 (multi-model range 170~193) million people were exposed to dengue, accounting for a quarter of the total population in Asian metropolises. More population is projected to be exposed to dengue in future. Population exposure in Asian metropolises might increase to 261 (range: 181~300) million, 268 (range: 228~326) million, 287 (range: 241~361) million, and 287 (range: 241~367) million, respectively, at 1·5°C, 2°C, 3°C, and 4°C warming levels. Metropolises with population of more than 10 million have 51% higher morbidity of dengue than smaller cities. Taking all urban population as a whole, increase of dengue fever cases in Asian metropolises is projected to be 90% (range: 77%~127%), 107% (range: 80%~180%), 152% (range: 119%~314%), and 186% (range: 146%~325%), respectively, under different warming scenarios, relative to 260 thousand per year in the reference period. Interpretation: Transmission of mosquitoes is determined mostly by temperature and precipitation conditions. Increasing temperature and precipitation in large parts of Asia in a global warming context expands the range of dengue fever. Although different cities show various climatic and socioeconomic characteristics, results consistently show that number of dengue cases may increase in Asian metropolises in future, which will bring huge challenges related to prevention and control of infectious diseases. Funding Statement: National Key Research and Development Program of China (2017YFA0603804) and the Cooperation Project of Cooperation Group (GZ 1486) in the Chinese and German Center of the Research Promotion, NSFC/DFG. Declaration of Interests: The authors declare no competing interests.

Epidemiological impact and cost-effectiveness of universal meningitis b vaccination among college students prior to college entry.

ObjectivesUniversity students are at significantly higher risk of serogroup B meningococcal (MenB) infection, which can result in debilitating sequelae and excessive healthcare usage. This study aimed to elucidate the impact of universal pre-enrollment vaccination on MenB outbreak probability and the cost-effectiveness in outbreak-only scenarios.MethodsWe developed an infectious disease transmission model to determine the number of outbreaks averted under universal vaccination and a Markov model to simulate the costs accrued and QALYs lost associated with infection. The analysis was done on a hypothetical population of 40,000 college students over a four-year time frame. We used the outputs of these two models to calculate the incremental cost-effectiveness ratio (ICER) of universal MenB vaccination from a societal perspective.ResultsWe find that the vaccination strategy was estimated to reduce MenB incidence by 63% and outbreak frequency rate by 90%. Under base case assumptions, the ICER of universal vaccination was $748,129 per QALY and in outbreak-only scenarios, it was cost-saving.ConclusionsUniversal vaccination is not cost-effective at the current low MenB incidence levels and vaccine price in the U.S., but it is cost-saving if outbreak is imminent.

Developing a decision-making framework for insect pest management: a case study using Aphis glycines (Hemiptera: Aphididae).

BACKGROUNDThe profitability of farming varies based on factors such as a crop's market value, input costs and occurrence of resistant pests, all capable of altering the value of pest management tactics in an integrated pest management program. We provide a framework for calculating expected yield and expected net revenue of pest management scenarios, using the soybean aphid (Aphis glycines) as a case study. Foliar insecticide and host‐plant resistance are effective management tactics for preventing yield loss from soybean aphid outbreaks; however, pyrethroid‐resistant aphid populations pose a management challenge for farmers. We evaluated eight scenarios relevant to soybean aphid management in Iowa with varying probabilities of aphid outbreaks and insecticide‐resistant aphids occurring.RESULTSOur equation suggests that insecticide use is profitable when the probability of an aphid outbreak is ≥29%, and soybean production will become more costly with increasing probability of pyrethroid‐resistant aphids. If farmers continue to use pyrethroids, they will not experience financial consequences from pyrethroid‐resistant aphids until the chance of insecticide resistance is 48%. Aphid‐resistant varieties provided consistent yield and offered the highest net revenue under all conditions.CONCLUSIONThis framework can be used for other crop–pest systems to evaluate the profitability of management tactics and investigate how resistance impacts revenue for farmers. Including the cost of resistance in crop budgets can help farmers and agronomic consultants comprehend these impacts and enhance decision‐making to increase revenue and curb resistance development.

Kinetic Monte Carlo model for the COVID-19 epidemic: Impact of mobility restriction on a COVID-19 outbreak.

As the coronavirus disease 2019 (COVID-19) spreads worldwide, epidemiological models have been employed to evaluate possible scenarios and gauge the efficacy of proposed interventions. Considering the complexity of disease transmission dynamics in cities, stochastic epidemic models include uncertainty in their treatment of the problem, allowing the estimation of the probability of an outbreak, the distribution of epidemic magnitudes, and their expected duration. In this sense, we propose a kinetic Monte Carlo epidemic model that focuses on demography and on age-structured mobility data to simulate the evolution of the COVID-19 outbreak in the capital of Brazil, Brasilia, under several scenarios of mobility restriction. We show that the distribution of epidemic outcomes can be divided into short-lived mild outbreaks and longer severe ones. We demonstrate that quarantines have the effect of reducing the probability of a severe outbreak taking place but are unable to mitigate the magnitude of these outbreaks once they happen. Finally, we present the probability of a particular trajectory in the epidemic progression resulting in a massive outbreak as a function of the cumulative number of cases at the end of each quarantine period, allowing for the estimation of the risk associated with relaxing mobility restrictions at a given time.

Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability.

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.

Making Waves Perspectives of Modelling and Monitoring of SARS-CoV-2 in Aquatic Environment for COVID-19 Pandemic.

Prevalence of SARS-CoV-2 in the aquatic environment pertaining to the COVID-19 pandemic has been a global concern. Though SARS-CoV-2 is known as a respiratory virus, its detection in faecal matter and wastewater demonstrates its enteric involvement resulting in vulnerable aquatic environment. Here, we provide the latest updates on wastewater-based epidemiology, which is gaining interest in the current situation as a unique tool of surveillance and monitoring of the disease. Transport pathways with its migration through wastewater to surface and subsurface waters, probability of infectivity and ways of inactivation of SARS-CoV-2 are discussed in detail. Epidemiological models, especially compartmental projections, have been explained with an emphasis on its limitation and the assumptions on which the future predictions of disease propagation are based. Besides, this review covers various predictive models to track and project disease spread in the future and gives an insight into the probability of a future outbreak of the disease.

Probability of Disease Extinction or Outbreak in a Stochastic Epidemic Model for West Nile Virus Dynamics in Birds.

Thresholds for disease extinction provide essential information for the prevention and control of diseases. In this paper, a stochastic epidemic model, a continuous-time Markov chain, for the transmission dynamics of West Nile virus in birds is developed based on the assumptions of its analogous deterministic model. The branching process is applied to derive the extinction threshold for the stochastic model and conditions for disease extinction or persistence. The probability of disease extinction computed from the branching process is shown to be in good agreement with the probability approximated from numerical simulations. The disease dynamics of both models are compared to ascertain the effect of demographic stochasticity on West Nile virus dynamics. Analytical and numerical results show differences in model predictions and asymptotic dynamics between stochastic and deterministic models that are crucial for the prevention of disease outbreaks. It is found that there is a high probability of disease extinction if the disease emerges from exposed mosquitoes unlike if it emerges from infectious mosquitoes and birds. Finite-time to disease extinction is estimated using sample paths and it is shown that the epidemic duration is shortest if the disease is introduced by exposed mosquitoes.

Modelling for risk and biosecurity related to forest health.

Modelling the invasion and emergence of forest pests and pathogens (PnPs) is necessary to quantify the risk levels for forest health and provide key information for policy makers. Here, we make a short review of the models used to quantify the invasion risk of exotic species and the emergence risk of native species. Regarding the invasion process, models tackle each invasion phase, e.g. pathway models to describe the risk of entry, species distribution models to describe potential establishment, and dispersal models to describe (human-assisted) spread. Concerning the emergence process, models tackle each process: spread or outbreak. Only a few spread models describe jointly dispersal, growth, and establishment capabilities of native species while some mechanistic models describe the population temporal dynamics and inference models describe the probability of outbreak. We also discuss the ways to quantify uncertainty and the role of machine learning. Overall, promising directions are to increase the models' genericity by parameterization based on meta-analysis techniques to combine the effect of species traits and various environmental drivers. Further perspectives consist in considering the models' interconnection, including the assessment of the economic impact and risk mitigation options, as well as the possibility of having multi-risks and the reduction in uncertainty by collecting larger fit-for-purpose datasets.

Have Private Equity Owned Nursing Homes Fared Worse Under COVID-19?

Over 40% of U.S. COVID-19 deaths occurred in nursing homes. Given increasing private equity (PE) ownership in healthcare and long-standing concerns that PE investors focus on profits to the detriment of patients, it is important to understand the impact of PE ownership during the COVID-19 pandemic. This study evaluates how PE acquisitions impacted the readiness and outcomes of nursing facilities during the onset of the COVID-19 pandemic. We relate PE ownership to COVID-19 cases, deaths, and personal protective equipment (PPE) shortages, controlling for facility characteristics, resident composition, local characteristics, and the severity of COVID-19 outbreak near the facility. PE ownership was associated with a mean decrease in the probability of confirmed resident cases by 7.1 percentage points ("pp") (p<0.01) and confirmed staff cases by 5.4 pp (p=0.01). PE was also associated with decreased probability of PPE shortages—including N95s (6.4 pp; p<0.01), surgical masks (7.6 pp; p<0.01), eyewear (4.8 pp; p<0.01), gowns (7.0 pp; p<0.01), gloves (3.3 pp; p=0.02), and hand sanitizer (2.3 pp; p=0.12). Facilities previously (but not presently) owned by PE firms did not fare similarly well. Prior PE ownership was associated with increased PPE shortages and, if anything, higher probability of resident outbreaks. Our results indicate that—contrary to a common media narrative—PE-owned facilities have actually fared better under the COVID-19 pandemic. They also suggest that the long-run consequences of PE ownership warrant further research.

Variation in SARS-CoV-2 Prevalence in U.S. Skilled Nursing Facilities.

OBJECTIVETo identify county and facility factors associated with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) outbreaks in skilled nursing facilities (SNFs).DESIGNCross‐sectional study linking county SARS‐CoV‐2 prevalence data, administrative data, state reports of SNF outbreaks, and data from Genesis HealthCare, a large multistate provider of post‐acute and long‐term care. State data are reported as of April 21, 2020; Genesis data are reported as of May 4, 2020.SETTING AND PARTICIPANTSThe Genesis sample consisted of 341 SNFs in 25 states, including a subset of 64 SNFs that underwent universal testing of all residents. The non‐Genesis sample included all other SNFs (n = 3,016) in the 12 states where Genesis operates that released the names of SNFs with outbreaks.MEASUREMENTSFor Genesis and non‐Genesis SNFs: any outbreak (one or more residents testing positive for SARS‐CoV‐2). For Genesis SNFs only: number of confirmed cases, SNF case fatality rate, and prevalence after universal testing.RESULTSOne hundred eighteen (34.6%) Genesis SNFs and 640 (21.2%) non‐Genesis SNFs had outbreaks. A difference in county prevalence of 1,000 cases per 100,000 (1%) was associated with a 33.6 percentage point (95% confidence interval (CI) = 9.6–57.7 percentage point; P = .008) difference in the probability of an outbreak for Genesis and non‐Genesis SNFs combined, and a difference of 12.5 cases per facility (95% CI = 4.4–20.8 cases; P = .003) for Genesis SNFs. A 10‐bed difference in facility size was associated with a 0.9 percentage point (95% CI = 0.6–1.2 percentage point; P < .001) difference in the probability of outbreak. We found no consistent relationship between Nursing Home Compare Five‐Star ratings or past infection control deficiency citations and probability or severity of outbreak.CONCLUSIONSLarger SNFs and SNFs in areas of high SARS‐CoV‐2 prevalence are at high risk for outbreaks and must have access to universal testing to detect cases, implement mitigation strategies, and prevent further potentially avoidable cases and related complications. J Am Geriatr Soc 68:2167–2173, 2020.

The 2018–2019 weak El Niño: Predicting the risk of a dengue outbreak in Machala, Ecuador

AbstractBetween October 2018 ‐ May 2019, sea surface temperature conditions in the central‐eastern tropical Pacific indicated a mild El Niño event. In May 2019, the global El Niño Southern Oscillation (ENSO) forecast consensus was that these generally weak warm patterns will persist at least until the end of the northern hemisphere summer. El Niño and its impact on local climatic conditions in southern coastal Ecuador influence the inter‐annual transmission of dengue fever in the region. In this study, we use an ENSO model to issue forecasts of El Niño for the year 2019, which are then used to predict local climate variables, precipitation and minimum temperature, in the city of Machala, Ecuador. All these forecasts are incorporated in a dengue transmission model, specifically developed and tested for this area, to produce out‐of‐sample predictions of dengue risk. Predictions are issued at the beginning of January 2019 for the whole year, thus providing the longest forecast lead time of 12 months. Preliminary results indicate that the mild and ongoing El Niño event did not provide the optimum climate conditions for dengue transmission, with the model predicting a very low probability of a dengue outbreak during the typical peak season in Machala in 2019. This is contrary to 2016, when a large El Niño event resulted in excess rainfall and warmer temperatures in the region, and a dengue outbreak occurred 3 months earlier than expected. This event was successfully predicted using a similar prediction framework to the one applied here. With the present study, we continue our efforts to build and test a climate service tool to issue early warnings of dengue outbreaks in the region.

Investigation of the food poisoning outbreak in girls hostel of medical college in Punjab

Background: Food safety is intricately woven with food security and nutrition. The “in‑living personnel” constitute a high‑risk group for food poisoning because of community kitchen practices. Quick response and action are vital to limit the morbidity and spread of the disease.Methods: A retrospective cohort study was conducted to investigate the food poisoning outbreak in private medical college. After the initial information of the sudden cases of vomiting and diarrhoea from girls’ hostel, epidemiological case sheet was developed for collecting the information from students. A line listing of all the probable cases was done. Common food items were identified. An environmental data recording was done. Attack rate, attributable risk and relative risk with 95% confidence interval were calculated for each food item to establish an association with the illness.Results: Out of the 97 students, who consumed the dinner, 8 girls presented with severe symptoms of the gastrointestinal upset along with other bodily symptoms, 60 students showed mild symptoms and 29 students did not show any of the symptoms of the disease. After analysis, palak paneer was found to be the food responsible for outbreak with most probable cause of contamination with Staph auerus.Conclusions: It has been found out in this investigation that whenever the sanitation conditions of the cooking area were compromised and negligence was played at the part of food handlers - it has always increased the probability of such outbreaks and sufferings of the people.

OvCWD: An agent‐based modeling framework for informing chronic wasting disease management in white‐tailed deer populations

Abstract1. Wildlife diseases are gathering attention worldwide due to their public health and economic or conservation impacts, and consequently, wildlife agencies are increasingly being tasked with disease surveillance and management responsibilities.2. Wildlife disease surveillance and management is challenging primarily due to complex processes of host population dynamics, some of which are inherently stochastic in nature. Individual heterogeneity in pathogen transmission further complicates our understanding of wildlife disease systems.3. Agent‐based models can incorporate stochasticity as well as individual heterogeneities and facilitate a better understanding of epidemiological processes in wildlife disease systems. Such an understanding is critical for designing and implementing effective disease control strategies.4. We have developed a customizable agent‐based modeling framework (OvCWD) that incorporates nonrandom and heterogeneous aspects of an emerging host–pathogen system (chronic wasting disease [CWD] in white‐tailed deer). Models in this framework link white‐tailed deer demography and behavior with CWD transmission dynamics. Insights gained from model explorations can help us better understand CWD spread in regional deer populations.5. We illustrate OvCWD application by deriving CWD outbreak probabilities for Montcalm County (Michigan, USA) deer population using alternate harvest strategies. The focus is on preemptive harvest strategies that can be implemented before CWD is detected in a population (pre‐establishment phase).6. OvCWD provides a defensible decision‐making context for designing locally relevant CWD control strategies. OvCWD can be readily adapted for simulating CWD in other cervid species as well as for simulating other cervid disease systems.

Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986–1990

Predicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of ‘skip’ years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in low R0 (secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic Susceptible–Infected–Recovered (SIR) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under ‘well-mixed’ conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models, including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.