Temperature modulates dengue virus epidemic growth rates through its effects on reproduction numbers and generation intervals

Epidemic growth rate, r, provides a more complete description of the potential for epidemics than the more commonly studied basic reproduction number, R₀, yet the former has never been described as a function of temperature for dengue virus or other pathogens with temperature-sensitive transmission. The need to understand the drivers of epidemics of these pathogens is acute, with arthropod-borne virus epidemics becoming increasingly problematic. We addressed this need by developing temperature-dependent descriptions of the two components of r—R₀ and the generation interval—to obtain a temperature-dependent description of r. Our results show that the generation interval is highly sensitive to temperature, decreasing twofold between 25 and 35°C and suggesting that dengue virus epidemics may accelerate as temperatures increase, not only because of more infections per generation but also because of faster generations. Under the empirical temperature relationships that we considered, we found that r peaked at a temperature threshold that was robust to uncertainty in model parameters that do not depend on temperature. Although the precise value of this temperature threshold could be refined following future studies of empirical temperature relationships, the framework we present for identifying such temperature thresholds offers a new way to classify regions in which dengue virus epidemic intensity could either increase or decrease under future climate change.

Recurrent, rapidly intensifying epidemics of dengue–the world’s most prevalent mosquito-borne viral disease–pose a challenge to healthcare systems throughout the tropical and subtropical world. An acute disease that tends to respond well to proper treatment, the sometimes intense nature of dengue epidemics has been known to overwhelm healthcare systems and elevate the morbidity and mortality of patients left without adequate medical treatment under peak epidemic conditions. Here, we quantify the temperature dependence of dengue epidemic intensity by quantifying two distinct determinants of epidemic growth rate: the average number of secondary infections arising from each primary infection and the average time between successive infections in humans. Our results show that the time between successive infections in humans decreases steadily with increasing temperatures, whereas the average number of secondary infections peaks at intermediate temperatures. Altogether, this suggests a peak temperature for dengue epidemic intensity. Applying this result to global temperature projections under future climate change scenarios suggests that dengue epidemics in many regions of the world could become more intense under future temperature increases.

Estimating unobserved SARS-CoV-2 infections in the United States

By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during its initial invasion of the United States remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, we estimated that 108,689 (95% posterior predictive interval [95% PPI]: 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model's predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.98; 95% PPI: 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States.

Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic

The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be "silently" spreading and provides a framework for understanding outbreak dynamics. VIDEO ABSTRACT.

Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. To disentangle contributions from multiple factors to inter-annual variation in epidemic size, we fitted a semi-mechanistic model to time series data from 2005-2015 and performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying factors: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the epidemic in 2014 was made possible by the combination of favorable conditions for all factors considered in our analysis.

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

Influenza immune escape under heterogeneous host immune histories

In a pattern called immune imprinting, individuals gain the strongest immune protection against the influenza strains encountered earliest in life. In many recent examples, differences in early infection history can explain birth year-associated differences in susceptibility (cohort effects). Susceptibility shapes strain fitness, but without a clear conceptual model linking host susceptibility to the identity and order of past infections general conclusions on the evolutionary and epidemic implications of cohort effects are not possible. Failure to differentiate between cohort effects caused by differences in the set, rather than the order (path), of past infections is a current source of confusion. We review and refine hypotheses for path-dependent cohort effects, which include imprinting. We highlight strategies to measure their underlying causes and emergent consequences.

Leveraging multiple data types to estimate the size of the Zika epidemic in the Americas

Several hundred thousand Zika cases have been reported across the Americas since 2015. Incidence of infection was likely much higher, however, due to a high frequency of asymptomatic infection and other challenges that surveillance systems faced. Using a hierarchical Bayesian model with empirically-informed priors, we leveraged multiple types of Zika case data from 15 countries to estimate subnational reporting probabilities and infection attack rates (IARs). Zika IAR estimates ranged from 0.084 (95% CrI: 0.067-0.096) in Peru to 0.361 (95% CrI: 0.214-0.514) in Ecuador, with significant subnational variability in every country. Totaling infection estimates across these and 33 other countries and territories, our results suggest that 132.3 million (95% CrI: 111.3-170.2 million) people in the Americas had been infected by the end of 2018. These estimates represent the most extensive attempt to determine the size of the Zika epidemic in the Americas, offering a baseline for assessing the risk of future Zika epidemics in this region.

Co-circulation and misdiagnosis led to underestimation of the 2015-2017 Zika epidemic in the Americas

During the 2015-2017 Zika epidemic, dengue and chikungunya-two other viral diseases with the same vector as Zika-were also in circulation. Clinical presentation of these diseases can vary from person to person in terms of symptoms and severity, making it difficult to differentially diagnose them. Under these circumstances, it is possible that numerous cases of Zika could have been misdiagnosed as dengue or chikungunya, or vice versa. Given the importance of surveillance data for informing epidemiological analyses, our aim was to quantify the potential extent of misdiagnosis during this epidemic. Using basic principles of probability and empirical estimates of diagnostic sensitivity and specificity, we generated revised estimates of reported cases of Zika that accounted for the accuracy of diagnoses made on the basis of clinical presentation with or without laboratory confirmation. Applying this method to weekly reported case data from 43 countries throughout Latin America and the Caribbean, we estimated that 944,700 (95% CrI: 884,900-996,400) Zika cases occurred when assuming all confirmed cases were diagnosed using molecular methods versus 608,400 (95% CrI: 442,000-821,800) Zika cases that occurred when assuming all confirmed cases were diagnosed using serological methods. Our results imply that misdiagnosis was more common in countries with proportionally higher reported cases of dengue and chikungunya, such as Brazil. Given that Zika, dengue, and chikungunya appear likely to co-circulate in the Americas and elsewhere for years to come, our methodology has the potential to enhance the interpretation of passive surveillance data for these diseases going forward. Likewise, our methodology could also be used to help resolve transmission dynamics of other co-circulating diseases with similarities in symptomatology and potential for misdiagnosis.

Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Burden is in the eye of the beholder: Sensitivity of yellow fever disease burden estimates to modeling assumptions

Estimates of disease burden are important for setting public health priorities. These estimates involve numerous modeling assumptions, whose uncertainties are not always well described. We developed a framework for estimating the burden of yellow fever in Africa and evaluated its sensitivity to modeling assumptions that are often overlooked. We found that alternative interpretations of serological data resulted in a nearly 20-fold difference in burden estimates (range of central estimates, 8.4 × 104 to 1.5 × 106 deaths in 2021–2030). Uncertainty about the vaccination status of serological study participants was the primary driver of this uncertainty. Even so, statistical uncertainty was even greater than uncertainty due to modeling assumptions, accounting for a total of 87% of variance in burden estimates. Combined with estimates that most infections go unreported (range of 95% credible intervals, 99.65 to 99.99%), our results suggest that yellow fever’s burden will remain highly uncertain without major improvements in surveillance.

PCV13-Serotype Breakthrough Pneumococcal Disease in Infants Receiving High-Valency Conjugate Vaccines: Population-Level Modeling in France

INTRODUCTION

Pneumococcal conjugate vaccines (PCVs) have been increasing in valency to protect against a larger number of serotypes; however, the addition of serotypes has come at the cost of reduced immunogenicity, which may lead to breakthrough disease.

METHODS

This study used a mathematical model to evaluate the impact of introducing routine vaccination with either PCV15 or PCV20 on breakthrough invasive pneumococcal disease (bIPD) incidence associated with PCV13 serotypes in infants aged 0-12 months in France. The model incorporated historical PCV introductions and calibrated age- and serotype-specific IPD data spanning 2000-2019. Serotype-specific vaccine effectiveness for PCV15 and PCV20 was predicted based on previously published analyses. The incidence of bIPD was evaluated across three serotype classes: PCV7 (serotypes 4, 6B, 9V, 14, 18C, 19F, and 23F), PCV13-nonPCV7-nonST3 (serotypes 1, 5, 6A, 7F, and 19A), and ST3 (serotype 3). Results were compared to IPD incidence in 2019.

RESULTS

Twenty years following introduction into the childhood immunization program, the routine use of PCV15 in a 2 + 1 regimen led to fewer PCV13-nonPCV7-nonST3-associated bIPD cases in infants than the use of PCV20 in either a 2 + 1 or 3 + 1 regimen. PCV15 reduced bIPD incidence in all three serotype classes (- 28% to - 89%) in infants, with the largest impact on ST3. PCV20 in both regimens resulted in more bIPD cases from PCV7 serotypes (+ 65% to + 350%), while PCV13-nonPCV7-nonST3 and ST3 bIPD cases increased in a 2 + 1 regimen (+ 28% and + 6%, respectively) but decreased in a 3 + 1 regimen (- 23% and - 30%, respectively), in infants.

CONCLUSIONS

Implementation of PCV15 in a 2 + 1 regimen could reduce bIPD incidence due to all PCV13 serotypes in infants, whereas PCV20 in a 2 + 1 regimen may lead to substantial increases in bIPD cases from PCV13 serotypes in infants. PCV20 in a 3 + 1 regimen could potentially lead to a resurgence of bIPD from PCV7 serotypes in infants.

A dynamic transmission model for assessing the impact of pneumococcal vaccination in the United States

Streptococcus pneumoniae (SP) is a bacterial pathogen that kills more than 300,000 children every year across the globe. Multiple vaccines exist that prevent pneumococcal disease, with each vaccine covering a variable number of the more than 100 known serotypes. Due to the high effectiveness of these vaccines, each new pneumococcal conjugate vaccine (PCV) introduction has resulted in a decrease in vaccine-type disease and a shift in the serotype distribution towards non-vaccine types in a phenomenon called serotype replacement. Here, an age-structured compartmental model was created that reproduced historical carriage transmission dynamics in the United States and was used to evaluate the population-level impact of new vaccine introductions into the pediatric population. The model incorporates co-colonization and serotype competition, which drives replacement of the vaccine types by the non-vaccine types. The model was calibrated to historical age- and serotype-specific invasive pneumococcal disease (IPD) data from the United States. Vaccine-specific coverage and effectiveness were integrated in accordance with the recommended timelines for each age group. Demographic parameters were derived from US-population-specific databases, while population mixing patterns were informed by US-specific published literature on age-group based mixing matrices. The calibrated model was then used to project the epidemiological impact of PCV15, a 15-valent pneumococcal vaccine, compared with the status quo vaccination with PCV13 and demonstrated the value of added serotypes in PCV15. Projections revealed that PCV15 would reduce IPD incidence by 6.04% (range: 6.01% to 6.06%) over 10 years when compared to PCV13.

Modelling the Epidemiological Impact of Different Adult Pneumococcal Vaccination Strategies in the United Kingdom

INTRODUCTION

Pneumococcal conjugate vaccines (PCVs) were first introduced in the paediatric United Kingdom (UK) immunisation programme in 2006 which led to significant declines in invasive pneumococcal disease (IPD) caused by targeted serotypes. Although paediatric PCVs provide some indirect protection to adults, a significant IPD burden remains in older adults. Here, we compared three adult (65+ years-old) and risk group (2-64-year-old) vaccination scenarios, namely a continuation of the status quo with PPSV23 vaccination, using the recently licensed-in-adults PCV20, or using the new adult-focused 21-valent PCV, V116.

METHODS

A population-level compartmental dynamic transmission model (DTM) was adapted to the UK setting. The model described Streptococcus pneumoniae carriage transmission dynamics and disease progression in the presence of age- and serotype-specific pneumococcal vaccines. We calibrated the DTM to age- and serotype-specific IPD data in the UK and used the model to make projections under the different adult vaccination scenarios while keeping PCV13 immunisation in children.

RESULTS

The calibrated model yielded reasonable parameter values and model fits that closely matched observed IPD dynamics. Among 65+ year-olds, 10-year model projections predicted that the routine use of V116 would reduce IPD incidence by 15.5%, while PCV20 would reduce IPD incidence by 8.9% and the continued use of PPSV23 would increase incidence by 3.83%. There was a notable decrease in IPD incidence in the serotypes unique to V116. In the serotypes included in PCV20 but not V116, the model did not predict a resurgence of IPD.

CONCLUSIONS

Projections revealed that in adults, V116 led to significantly greater reductions in IPD in the 65+ year-old population compared with PCV20 or PPSV23.