Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis

AbstractBackground Zika virus (ZIKV) emerged in Latin America & the Caribbean (LAC) region in 2013, and has had serious implications for population health in the region. In 2016, the World Health Organization declared the ZIKV outbreak a Public Health Emergency of International Concern following a cluster of associated neurological disorders and neonatal malformations. In 2017, Zika cases declined, but future incidence in LAC remains uncertain due to gaps in our understanding, considerable variation in surveillance and a lack of a comprehensive collation of data from affected countries.Methods Our analysis combines information on confirmed and suspected Zika cases across LAC countries and a spatio-temporal dynamic transmission model for ZIKV infection to determine key transmission parameters and projected incidence in 91 major cities within 35 countries. Seasonality was determined by spatio-temporal estimates of Aedes aegypti vector capacity. We used country and state-level data from 2015 to mid-2017 to infer key model parameters, country-specific disease reporting rates, and the 2018 projected incidence. A 10-fold cross-validation approach was used to validate parameter estimates to out-of-sample epidemic trajectories.Results There was limited transmission in 2015, but in 2016 and 2017 there was sufficient opportunity for wide-spread ZIKV transmission in most cities, resulting in the depletion of susceptible individuals. We predict that the highest number of cases in 2018 within some Brazilian States (Sao Paulo and Rio de Janeiro), Colombia and French Guiana, but the estimated number of cases were no more than a few hundred. Model estimates of the timing of the peak in incidence were correlated (p<0.05) with the reported peak in incidence. The reporting rate varied across countries, with lower reporting rates for those with only confirmed cases compared to those who reported both confirmed and suspected cases.Conclusions The findings suggest that the ZIKV epidemic is by and large over, with incidence projected to be low in most cities in LAC in 2018. Local low levels of transmission are probable but the estimated rate of infection suggests that most cities have a population with high levels of herd immunity.

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Modeling Zika Vaccination Combined With Vector Interventions in DoD Populations

Military Medicine ◽

10.1093/milmed/usaa340 ◽

2021 ◽

Vol 186 (Supplement_1) ◽

pp. 82-90

Author(s):

Colleen Burgess ◽

Lis Nelis ◽

Cassie Huang

Keyword(s):

Mosquito Control ◽

Cost Benefit Analysis ◽

Herd Immunity ◽

Febrile Illness ◽

Sensitivity Analyses ◽

Transmission Model ◽

Model Parameters ◽

Military Installations ◽

Low Level ◽

Limited Effectiveness

ABSTRACT Introduction Zika virus (ZIKV) is a mild febrile illness generally transmitted via the bite of infected Aedes species mosquitoes, including Aedes aegypti, with the potential to cause neurological complications. Nearly 200 U.S. military installations are located within areas where Aedes mosquitos are found, putting thousands of personnel at risk for infection with ZIKV. This analysis aims to quantify the benefits of interventions, including vaccination, to decrease the risk of ZIKV on U.S. military installations. Methods The authors developed a dynamic transmission model to test the “effectiveness” of vaccination, personal protective measures (PPM), and mosquito control at reducing morbidity within U.S. military populations. ZIKV transmission was modeled as a compartmental susceptible-exposed-infected-recovered model tracking interactions between humans and mosquitos and incorporating seasonality of mosquito populations and the potential for herd immunity. The model included two-dose vaccination as well as symptomatic and asymptomatic infection. The model was calibrated against 2016 public health data in Puerto Rico; sensitivity analyses were performed on model parameters and interventions. Results The greatest reduction in total modeled ZIKV cases resulted from vaccination combined with mosquito control and PPM. All three interventions at their highest estimated level of efficiency reduced ZIKV cases by 99.9% over the baseline case of low-level adherence to PPM. The addition of vaccination had limited additional benefit over effective vector control and PPM since the significant lag to vaccine-induced protection limited effectiveness of vaccination. Conclusions Given the current vaccine, the model predicted that up to 92.8% of Zika cases occurring in deployment settings over a 10-year period could be prevented by adding vaccination to current low-level PPM. Combining vaccination with other interventions can reduce cases further. A location-specific cost-benefit analysis would be a valuable contribution to outbreak control policy as it could evaluate the economic impact of the interventions versus the reduced level of illness and downtime in this setting.

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Epidemiological and ecological determinants of Zika virus transmission in an urban setting

eLife ◽

10.7554/elife.29820 ◽

2017 ◽

Vol 6 ◽

Cited By ~ 44

Author(s):

José Lourenço ◽

Maricelia Maia de Lima ◽

Nuno Rodrigues Faria ◽

Andrew Walker ◽

Moritz UG Kraemer ◽

...

Keyword(s):

Public Health ◽

Zika Virus ◽

Herd Immunity ◽

Virus Transmission ◽

Health Impact ◽

Urban Setting ◽

Health Concern ◽

Transmission Model ◽

Global Public Health ◽

Transmission Potential

The Zika virus has emerged as a global public health concern. Its rapid geographic expansion is attributed to the success of Aedes mosquito vectors, but local epidemiological drivers are still poorly understood. Feira de Santana played a pivotal role in the Chikungunya epidemic in Brazil and was one of the first urban centres to report Zika infections. Using a climate-driven transmission model and notified Zika case data, we show that a low observation rate and high vectorial capacity translated into a significant attack rate during the 2015 outbreak, with a subsequent decline in 2016 and fade-out in 2017 due to herd-immunity. We find a potential Zika-related, low risk for microcephaly per pregnancy, but with significant public health impact given high attack rates. The balance between the loss of herd-immunity and viral re-importation will dictate future transmission potential of Zika in this urban setting.

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Modelling biocide and herbicide concentrations in catchments of the Rhine basin

10.5194/hess-2017-628 ◽

2017 ◽

Cited By ~ 2

Author(s):

Andreas Moser ◽

Devon Wemyss ◽

Ruth Scheidegger ◽

Fabrizio Fenicia ◽

Mark Honti ◽

...

Keyword(s):

Water Quality ◽

Land Use ◽

Parameter Estimates ◽

Model Parameters ◽

Organic Micropollutants ◽

Background Concentrations ◽

Quality Models ◽

Spatially Distributed ◽

Spatio Temporal ◽

Water Quality Models

Abstract. Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which requires large sampling and analytical efforts to obtain representative data on the actual water quality. Models can also be used to predict information to which degree streams are affected by these pollutants. However, spatially distributed modeling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas where predictions are needed. To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge. The model was calibrated with data sets from three different small catchments (0.5–24.6 km2) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was validated for different years on 12 catchments of much larger size (31–160 000 km2) without any modification. For most compound-catchment combinations, the model predictions revealed a satisfactory correlation (median r2: 0.5) with the observations and the peak concentrations mostly predicted within a factor of two to four (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated, the predictions of the actual timing of peak concentrations however, was generally poor. Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinise model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of background concentrations to land use intensity. Despite these limitations the findings indicate that key drivers and processes are reasonably well approximated by the model and that such a simple model that includes land use as a proxy for compound use, weather data for the timing of herbicide applications and discharge or precipitation as drivers for transport is sufficient to predict timing and level of peak concentrations within a factor of two to three in a spatially distributed manner at the scale of large river basins.

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Are we ready for Taenia solium cysticercosis elimination in sub-Saharan Africa?

Parasitology ◽

10.1017/s0031182016000500 ◽

2016 ◽

Vol 144 (1) ◽

pp. 59-64 ◽

Cited By ~ 16

Author(s):

MARIA VANG JOHANSEN ◽

CHIARA TREVISAN ◽

SARAH GABRIËL ◽

PASCAL MAGNUSSEN ◽

UFFE CHRISTIAN BRAAE

Keyword(s):

Latin America ◽

Current Knowledge ◽

Taenia Solium ◽

Sub Saharan Africa ◽

World Health ◽

Transmission Model ◽

Small Community ◽

Porcine Cysticercosis ◽

Sub Saharan ◽

Health Organization

SUMMARYThe World Health Organization announced in November 2014 at the fourth international meeting on ‘the control of neglected zoonotic diseases – from advocacy to action’, that intervention tools for eliminating Taenia solium taeniosis/cysticercosis (TSTC) are in place. The aim of this work was to elucidate theoretical outcomes of various control options suggested for TSTC elimination in sub-Saharan Africa (SSA) over a 4-year period. Our current knowledge regarding T. solium epidemiology and control primarily builds on studies from Latin America. A simple transmission model – built on data from Latin America – has been used to predict the effect of various interventions such as mass treatment of humans, vaccination and treatment of pigs, and health education of communities, potentially leading to change in bad practices and reducing transmission risks. Based on simulations of the transmission model, even a 4-year integrated One Health approach fails to eliminate TSTC from a small community and in all simulations, the prevalence of human taeniosis and porcine cysticercosis start to rise as soon as the programmes end. Our current knowledge regarding transmission and burden of TSTC in SSA is scarce and while claiming to be tool ready, the selection of diagnostic and surveillance tools, as well as the algorithms and stepwise approaches for control and elimination of TSTC remain major challenges.

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Surveillance of adverse events following immunisation in Australia annual report, 2017

Communicable Diseases Intelligence ◽

10.33321/cdi.2019.43.29 ◽

2019 ◽

Vol 43 ◽

Cited By ~ 4

Author(s):

Aditi Dey ◽

Han Wang ◽

Helen Quinn ◽

Rona Hiam ◽

Nicholas Wood ◽

...

Keyword(s):

Adverse Events ◽

Annual Report ◽

Injection Site Reaction ◽

Reporting Rate ◽

Vaccination Programs ◽

National Immunisation ◽

National Immunisation Program ◽

Conjugate Vaccination ◽

Reporting Rates ◽

To Receive

This report summarises Australian passive surveillance data for adverse events following immunisation (AEFI) for 2017 reported to the Therapeutic Goods Administration and describes reporting trends over the 18-year period 1 January 2000 to 31 December 2017. There were 3,878 AEFI records for vaccines administered in 2017; an annual AEFI reporting rate of 15.8 per 100,000 population. There was a 12% increase in the overall AEFI reporting rate in 2017 compared with 2016. This increase in reported adverse events in 2017 compared to the previous year was likely due to the introduction of the zoster vaccine (Zostavax®) provided free for people aged 70–79 years under the National Immunisation Program (NIP) and also the state- and territory-based meningococcal ACWY conjugate vaccination programs. AEFI reporting rates for most other individual vaccines in 2017 were similar to 2016. The most commonly reported reactions were injection site reaction (34%), pyrexia (17%), rash (15%), vomiting (8%) and pain (7%). The majority of AEFI reports (88%) described non-serious events. Two deaths were reported that were determined to have a causal relationship with vaccination; they occurred in immunocompromised people contraindicated to receive the vaccines.

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Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

Scientific Reports ◽

10.1038/s41598-021-90347-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jie Zhu ◽

Blanca Gallego

Keyword(s):

Public Health ◽

Disease Transmission ◽

Clustering Algorithm ◽

Health Interventions ◽

Transmission Model ◽

Model Parameters ◽

Public Health Interventions ◽

Forecasting Accuracy ◽

Time Lagged ◽

The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

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Programme for Harmonization to the International Scale in Latin America for BCR-ABL1 quantification in CML patients: findings and recommendations

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2019-1283 ◽

2020 ◽

Vol 58 (12) ◽

pp. 2025-2035

Author(s):

María Sol Ruiz ◽

María Belén Sánchez ◽

Yuly Masiel Vera Contreras ◽

Evangelina Agrielo ◽

Marta Alonso ◽

...

Keyword(s):

Latin America ◽

Myeloid Leukemia ◽

World Health ◽

Molecular Response ◽

Limit Of Quantification ◽

International Scale ◽

Deep Molecular Response ◽

Health Organization ◽

First Time

AbstractObjectivesThe quantitation of BCR-ABL1 mRNA is mandatory for chronic myeloid leukemia (CML) patients, and RT-qPCR is the most extensively used method in testing laboratories worldwide. Nevertheless, substantial variation in RT-qPCR results makes inter-laboratory comparability hard. To facilitate inter-laboratory comparative assessment, an international scale (IS) for BCR-ABL1 was proposed.MethodsThe laboratory-specific conversion factor (CF) to the IS can be derived from the World Health Organization (WHO) genetic reference panel; however, this material is limited to the manufacturers to produce and calibrate secondary reference reagents. Therefore, we developed secondary reference calibrators, as lyophilized cellular material, aligned to the IS. Our purpose was both to re-evaluate the CF in 18 previously harmonized laboratories and to propagate the IS to new laboratories.ResultsOur field trial including 30 laboratories across Latin America showed that, after correction of raw BCR-ABL1/ABL1 ratios using CF, the relative mean bias was significantly reduced. We also performed a follow-up of participating laboratories by annually revalidating the process; our results support the need for continuous revalidation of CFs. All participating laboratories also received a calibrator to determine the limit of quantification (LOQ); 90% of them could reproducibly detect BCR-ABL1, indicating that these laboratories can report a consistent deep molecular response. In addition, aiming to investigate the variability of BCR-ABL1 measurements across different RNA inputs, we calculated PCR efficiency for each individual assay by using different amounts of RNA.ConclusionsIn conclusion, for the first time in Latin America, we have successfully organized a harmonization platform for BCR-ABL1 measurement that could be of immediate clinical benefit for monitoring the molecular response of patients in low-resource regions.

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High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

Nature Communications ◽

10.1038/s41467-021-25169-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ellen Brooks-Pollock ◽

Hannah Christensen ◽

Adam Trickey ◽

Gibran Hemani ◽

Emily Nixon ◽

...

Keyword(s):

Prediction Interval ◽

Mitigation Strategies ◽

Control Measures ◽

Transmission Model ◽

Model Parameters ◽

Infection Rates ◽

First Year Students ◽

Transmission Potential ◽

Plausible Model ◽

Effective Option

AbstractControlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

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Vaccination Coverage for Routine Vaccines and Herd Immunity Levels against Measles and Pertussis in the World in 2019

Vaccines ◽

10.3390/vaccines9030256 ◽

2021 ◽

Vol 9 (3) ◽

pp. 256

Author(s):

Pedro Plans-Rubió

Keyword(s):

Vaccination Coverage ◽

Action Plan ◽

Herd Immunity ◽

World Health ◽

Immunization Programs ◽

National Immunization ◽

The World ◽

Measles Vaccination ◽

The Mean ◽

Health Organization

In 2012, the World Health Organization (WHO) established the Global Vaccine Action Plan with the objective to promote essential vaccinations in all countries and achieve at least 90% vaccination coverage for all routine vaccines by 2020. The study assessed the mean percentages of vaccination coverage in 2019 for 13 routine vaccines, vaccination coverage variation from 2015 to 2019, and herd immunity levels against measles and pertussis in 2019 in countries and regions of WHO. In 2019, the mean percentages of vaccination coverage were lower than 90% for 10 (78.9%) routine vaccines. The mean percentages of vaccination coverage also decreased from 2015 to 2019 for six (46.2%) routine vaccines. The prevalence of individuals with vaccine-induced measles immunity in the target measles vaccination population was 88.1%, and the prevalence of individuals with vaccine-induced pertussis immunity in the target pertussis vaccination population was 81.1%. Herd immunity against measles viruses with Ro = 18 was established in 63 (32.5%) countries but not established in any region. Herd immunity against pertussis agents was not established in any country and in any region of WHO. National immunization programs must be improved to achieve ≥90% vaccination coverage in all countries and regions. Likewise, it is necessary to achieve ≥95% vaccination coverage with two doses of measles vaccines and three doses of pertussis vaccines in all countries and regions.

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Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

Journal of Hydroinformatics ◽

10.2166/hydro.2008.016 ◽

2008 ◽

Vol 10 (2) ◽

pp. 153-162 ◽

Cited By ~ 2

Author(s):

B. G. Ruessink

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Parameter Estimates ◽

Model Parameters ◽

Parameter Stability ◽

Small Uncertainty ◽

Best Fit ◽

Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

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