Quantifying Zika: Advancing the Epidemiology of Zika With Quantitative Models

Guillain-Barré Syndrome Following Zika Virus Infection Is Associated With a Diverse Spectrum of Peripheral Nerve Reactive Antibodies

BACKGROUND AND OBJECTIVES

Recent outbreaks of Zika virus (ZIKV) in South and Central America have highlighted significant neurologic side effects. Concurrence with the inflammatory neuropathy Guillain-Barré syndrome (GBS) is observed in 1:4,000 ZIKV cases. Whether the neurologic symptoms of ZIKV infection are immune mediated is unclear. We used rodent and human live cellular models to screen for anti-peripheral nerve reactive IgG and IgM autoantibodies in the sera of patients with ZIKV with and without GBS.

METHODS

In this study, 52 patients with ZIKV-GBS were compared with 134 ZIKV-infected patients without GBS and 91 non-ZIKV controls. Positive sera were taken forward for target identification by immunoprecipitation and mass spectrometry, and candidate antigens were validated by ELISA and cell-based assays. Autoantibody reactions against glycolipid antigens were also screened on an array.

RESULTS

Overall, IgG antibody reactivities to rat Schwann cells (SCs) (6.5%) and myelinated cocultures (9.6%) were significantly higher, albeit infrequent, in the ZIKV-GBS group compared with all controls. IgM antibody immunoreactivity to dorsal root ganglia neurones (32.3%) and SCs (19.4%) was more frequently observed in the ZIKV-GBS group compared with other controls, whereas IgM reactivity to cocultures was as common in ZIKV and non-ZIKV sera. Strong axonal-binding ZIKV-GBS serum IgG antibodies from 1 patient were confirmed to react with neurofascin 155 and 186. Serum from a ZIKV-infected patient without GBS displayed strong myelin-binding and putative antilipid antigen reaction characteristics. There was, however, no significant association of ZIKV-GBS with any known antiglycolipid antibodies.

DISCUSSION

Autoantibody responses in ZIKV-GBS target heterogeneous peripheral nerve antigens suggesting heterogeneity of the humoral immune response despite a common prodromal infection.

Viral genome-based Zika virus transmission dynamics in a paediatric cohort during the 2016 Nicaragua epidemic

BACKGROUND

Nicaragua experienced a large Zika epidemic in 2016, with up to 50% of the population in Managua infected. With the domesticated Aedes aegypti mosquito as its vector, it is widely assumed that Zika virus transmission occurs within the household and/or via human mobility. We investigated these assumptions by using viral genomes to trace Zika transmission spatially.

METHODS

We analysed serum samples from 119 paediatric Zika cases participating in the long-standing Paediatric Dengue Cohort Study in Managua, which was expanded to include Zika in 2015. An optimal spanning directed tree was constructed by minimizing the differences in viral sequence diversity composition between patient nodes, where low-frequency variants were used to increase the resolution of the inferred Zika outbreak dynamics.

FINDINGS

Out of the 18 houses where pairwise difference in sample collection dates among all the household members was within 30 days, we only found two where viruses from individuals within the same household were up to 10th-most closely linked to each other genetically. We also identified a substantial number of transmission events involving long geographical distances (n=30), as well as potential super-spreading events in the estimated transmission tree.

INTERPRETATION

Our finding highlights that community transmission, often involving long geographical distances, played a much more important role in epidemic spread than within-household transmission.

FUNDING

This study was supported by an NUS startup grant (OMS) and grants R01 AI099631 (AB), P01 AI106695 (EH), P01 AI106695-03S1 (FB), and U19 AI118610 (EH) from the US National Institutes of Health.

Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA

The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state’s response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.

Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities

As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users' minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.

Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016

Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators: suspected arboviral cases, suspected Zika-associated Guillain-Barré Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33^rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI: 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI: 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI: 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response.

Zika virus (ZIKV) infections, like many infections, are generally underreported due to asymptomatic, mild, or unrecognized cases. Using available surveillance indicators reflecting imperfect proxies of infection, we developed a modeling approach to estimate the weekly incidence of infection by combining independent surveillance indicators and assumptions about system-specific reporting biases in a Bayesian framework. Using our approach, we estimated that approximately 890,000 people in the population were infected with Zika in Puerto Rico in 2016, much higher than the 36,316 reported confirmed infections. Our framework has broad application to other diseases where cases may be underreported through traditional disease surveillance and can provide near real-time changes in incidences.

Mapping the cryptic spread of the 2015-2016 global Zika virus epidemic

BACKGROUND

Zika virus (ZIKV) emerged as a global epidemic in 2015-2016 from Latin America with its true geographical extent remaining unclear due to widely presumed underreporting. The identification of locations with potential and unknown spread of ZIKV is a key yet understudied component for outbreak preparedness. Here, we aim to identify locations at a high risk of cryptic ZIKV spread during 2015-2016 to further the understanding of the global ZIKV epidemiology, which is critical for the mitigation of the risk of future epidemics.

METHODS

We developed an importation simulation model to estimate the weekly number of ZIKV infections imported in each susceptible spatial unit (i.e. location that did not report any autochthonous Zika cases during 2015-2016), integrating epidemiological, demographic, and travel data as model inputs. Thereafter, a global risk model was applied to estimate the weekly ZIKV transmissibility during 2015-2016 for each location. Finally, we assessed the risk of onward ZIKV spread following importation in each susceptible spatial unit to identify locations with a high potential for cryptic ZIKV spread during 2015-2016.

RESULTS

We have found 24 susceptible spatial units that were likely to have experienced cryptic ZIKV spread during 2015-2016, of which 10 continue to have a high risk estimate within a highly conservative scenario, namely, Luanda in Angola, Banten in Indonesia, Maharashtra in India, Lagos in Nigeria, Taiwan and Guangdong in China, Dakar in Senegal, Maputo in Mozambique, Kinshasa in Congo DRC, and Pool in Congo. Notably, among the 24 susceptible spatial units identified, some have reported their first ZIKV outbreaks since 2017, thus adding to the credibility of our results (derived using 2015-2016 data only).

CONCLUSION

Our study has provided valuable insights into the potentially high-risk locations for cryptic ZIKV circulation during the 2015-2016 pandemic and has also laid a foundation for future studies that attempt to further narrow this key knowledge gap. Our modelling framework can be adapted to identify areas with likely unknown spread of other emerging vector-borne diseases, which has important implications for public health readiness especially in resource-limited settings.

Brazil's Missing Infants: Zika Risk Changes Reproductive Behavior

Zika virus epidemics have potential large-scale population effects. Controlled studies of mice and nonhuman primates indicate that Zika affects fecundity, raising concerns about miscarriage in human populations. In regions of Brazil, Zika risk peaked months before residents learned about the epidemic and its relation to congenital anomalies. This spatiotemporal variation supports analysis of both biological effects of Zika infection on fertility and the effects of learning about Zika risk on reproductive behavior. Causal inference techniques used with vital statistics indicate that the epidemic caused reductions in birth cohort size of approximately one-quarter 18 months after Zika infection risk peaked but 10 months after public health messages advocated childbearing delay. The evidence is consistent with small but not statistically detectable biological reductions in fecundity, as well as large strategic changes in reproductive behavior to temporally align childbearing with reduced risk to infant health. The behavioral effects are larger for more-educated and older women, which may reflect facilitated access to information and to family planning services within high-risk, mosquito-infested urban locations as well as perceptions about the opportunity costs of risks to pregnancy and infant survival.

Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action

Introduction:

High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications.

Methods:

We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors.

Results:

A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies.

Conclusions:

This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.

A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern

INTRODUCTION

Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was.

METHODS

To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers.

RESULTS

2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies.

CONCLUSIONS

Many ZIKV predictions were published during the 2016-2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics.

Zika Virus and Future Research Directions

There was a dramatic upsurge in research activity after the recognition of Zika virus (ZIKV) transmission in South America in 2015 and its causal relationship to devastating anomalies in newborn infants. Progress in this area required a community of arbovirologists poised to refocus their research efforts and rapidly characterize the features of ZIKV transmission and infection through diverse multidisciplinary collaborations. Significant gaps remain in our knowledge of the natural history of ZIKV infection, its effects on neurodevelopment, modes and risk of transmission, and its interrelationship with other arbovirus infections. Development of effective countermeasures, such as therapeutics and an effective vaccine, are also research priorities. Lessons learned from our research response to ZIKV may help public health officials plan for the next emerging infectious disease threat.The last 18 months have witnessed one of the most rapid and coordinated research responses against an emerging disease to date. Zika virus, a pathogen that has been known since 1947 but poorly studied until recently because it was believed to only cause a mild infection, has rapidly become the object of intense investigation by the international research community since the link between infection and severe congenital disease was announced by Brazilian authorities in November 2015. According to PubMed, the total number of ZIKV-related publications skyrocketed from 117 in 2015 to 3253 in August of 2017. This supplement summarizes the tremendous progress that has been made since 2015 to elucidate the biology of this virus, its various disease manifestations in humans and animals, the diverse routes by which it is transmitted, and the role of various mosquito vectors in the recent outbreaks. In addition, several efforts have been initiated to develop new diagnostics, therapeutics, vaccines, and vector control strategies to better detect, treat, and prevent this important infection. There are 3 factors that contributed to the rapid progress in ZIKV research: (1) the availability of dedicated funding for ZIKV research; (2) the prior existence of both flavivirologists and maternal-child health researchers who were poised to tackle this new public health challenge; and (3) the high level of coordination and collaboration between different research agencies worldwide.Despite the significant progress, many significant questions remain to be addressed to accelerate the development of effective ZIKV countermeasures and increase our preparedness against this significant public health threat. Some of the most pressing scientific gaps that need to be addressed to advance the field are summarized below.

Predicting congenital rubella syndrome in Japan, 2018-2019

OBJECTIVES

A rubella epidemic has been ongoing in Japan since August 2018. In the present study, we aimed to predict the likely size of a congenital rubella syndrome (CRS) epidemic during 2018-19.

METHODS

The expected number of CRS cases was estimated using an integral equation based on age-specific incidence of rubella among adult women, the time delay from gestational age of infection to diagnosis of CRS, and distribution of the mothers' age at delivery. We used epidemic data during 2012-14 to parameterize the model and applied this in the prediction for 2018-19.

RESULTS

In analyzing the 2012-14 epidemic data, the mean delay from the mother's infection to diagnosis was estimated at 24.2weeks (95% confidence interval (CI): 20.7, 28.1). Applying the parameterized model, together with the more than 480 rubella cases in women in 2018 as well as delayed mother's age at delivery in 2017, we determined that the expected number of CRS cases would be 9.7 (95% CI: 6.5, 12.5) cases. As the epidemic is ongoing, the cumulative number of CRS cases could potentially reach 96.8 (95% CI: 65.3, 125.5) cases, if rubella cases in adult women rose to 10 times the number by week 49 in 2018.

CONCLUSIONS

CRS is expected to occur an average of 24weeks following the mother's infection with rubella virus. Accounting for an increase to 650 cases in women by week 5 in 2019, the expected number of CRS cases during 2018-19 has already exceeded 13 cases, as of week 5 in 2019.

Quantifying the risk of local Zika virus transmission in the contiguous US during the 2015-2016 ZIKV epidemic

BACKGROUND

Local mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties in the contiguous United States (US), prompting the issuance of travel, prevention, and testing guidance across the contiguous US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US.

METHODS

We present a framework for the projection of ZIKV autochthonous transmission in the contiguous US during the 2015-2016 epidemic using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental, and detailed population data. The model generates an ensemble of travel-related case counts and simulates their potential to have triggered local transmission at the individual level in the 2015-2016 ZIKV epidemic.

RESULTS

We estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025° × 0.025° cell level across the contiguous US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the epidemic observed during the years 2015-2016. The high spatial and temporal resolution of the model allows us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk was spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the contiguous US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission were among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission and identify the key introduction routes and their contributions to ZIKV transmission in the contiguous US.

CONCLUSIONS

This framework provides quantitative risk estimates, fully captures the stochasticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic cases. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US.