Maternal Zika virus infection and newborn microcephaly-an analysis of the epidemiological evidence

Mathematical modeling applied to epidemics: an overview

This work presents an overview of the evolution of mathematical modeling applied to the context of epidemics and the advances in modeling in epidemiological studies. In fact, mathematical treatments have contributed substantially in the epidemiology area since the formulation of the famous SIR (susceptible-infected-recovered) model, in the beginning of the 20th century. We presented the SIR deterministic model and we also showed a more realistic application of this model applying a stochastic approach in complex networks. Nowadays, computational tools, such as big data and complex networks, in addition to mathematical modeling and statistical analysis, have been shown to be essential to understand the developing of the disease and the scale of the emerging outbreak. These issues are fundamental concerns to guide public health policies. Lately, the current pandemic caused by the new coronavirus further enlightened the importance of mathematical modeling associated with computational and statistical tools. For this reason, we intend to bring basic knowledge of mathematical modeling applied to epidemiology to a broad audience. We show the progress of this field of knowledge over the years, as well as the technical part involving several numerical tools.

Is the ZIKV Congenital Syndrome and Microcephaly Due to Syndemism with Latent Virus Coinfection?

The emergence of the Zika virus (ZIKV) mirrors its evolutionary nature and, thus, its ability to grow in diversity or complexity (i.e., related to genome, host response, environment changes, tropism, and pathogenicity), leading to it recently joining the circle of closed congenital pathogens. The causal relation of ZIKV to microcephaly is still a much-debated issue. The identification of outbreak foci being in certain endemic urban areas characterized by a high-density population emphasizes that mixed infections might spearhead the recent appearance of a wide range of diseases that were initially attributed to ZIKV. Globally, such coinfections may have both positive and negative effects on viral replication, tropism, host response, and the viral genome. In other words, the possibility of coinfection may necessitate revisiting what is considered to be known regarding the pathogenesis and epidemiology of ZIKV diseases. ZIKV viral coinfections are already being reported with other arboviruses (e.g., chikungunya virus (CHIKV) and dengue virus (DENV)) as well as congenital pathogens (e.g., human immunodeficiency virus (HIV) and cytomegalovirus (HCMV)). However, descriptions of human latent viruses and their impacts on ZIKV disease outcomes in hosts are currently lacking. This review proposes to select some interesting human latent viruses (i.e., herpes simplex virus 2 (HSV-2), Epstein-Barr virus (EBV), human herpesvirus 6 (HHV-6), human parvovirus B19 (B19V), and human papillomavirus (HPV)), whose virological features and co-exposition with ZIKV may provide evidence of the syndemism process, shedding some light on the emergence of the ZIKV-induced global congenital syndrome in South America.

Could simulation methods solve the curse of sparse data within clinical studies of antibiotic resistance?

Infectious disease (ID) physicians and ID pharmacists commonly confront therapeutic questions relating to antibiotic resistance. Randomized controlled trial data are few and meta-analytic-based approaches to develop the evidence-base from several small studies that might relate to an antibiotic resistance question are not simple. The overriding challenge is the sparsity of data which is problematic for traditional frequentist methods, being the paradigm underlying the derivation of ‘P value’ inferential statistics. In other sparse data contexts, simulation methods enable answers to key questions that are meaningful, quantitative and potentially relevant. How these simulation methods ‘work’ and how Bayesian-based methods, being not ‘P value based’, can facilitate simulation are reviewed. These methods are becoming increasingly accessible. This review highlights why sparse data is less of an issue within Bayesian versus frequentist paradigms. A fictional pharmacokinetic study with sparse data illustrates a simplistic application of Bayesian and simulation methods to antibiotic dosing. Whether within epidemiological projections or clinical studies, simulation methods are likely to play an increasing role in antimicrobial resistance research within both hospital and community studies of either rare infectious disease or infections within specific population groups.

Recent progresses and remaining challenges for the detection of Zika virus

Zika virus (ZIKV) has emerged as a particularly notorious mosquito-borne flavivirus, which can lead to a devastating congenital syndrome in the fetuses of pregnant mothers (e.g., microcephaly, spasticity, craniofacial disproportion, miscarriage, and ocular abnormalities) and cause the autoimmune disorder Guillain-Barre' syndrome of adults. Due to its severity and rapid dispersal over several continents, ZIKV has been acknowledged to be a global health concern by the World Health Organization. Unfortunately, the ZIKV has recently resurged in India with the potential for devastating effects. Researchers from all around the world have worked tirelessly to develop effective detection strategies and vaccines for the prevention and control of ZIKV infection. In this review, we comprehensively summarize the most recent research into ZIKV, including the structural biology and evolution, historical overview, pathogenesis, symptoms, and transmission. We then focus on the detection strategies for ZIKV, including viral isolation, serological assays, molecular assays, sensing methods, reverse transcription loop mediated isothermal amplification, transcription-mediated amplification technology, reverse transcription strand invasion based amplification, bioplasmonic paper-based device, and reverse transcription isothermal recombinase polymerase amplification. To conclude, we examine the limitations of currently available strategies for the detection of ZIKV, and outline future opportunities and research challenges.

Zika virus infection as a cause of congenital brain abnormalities and Guillain-Barré syndrome: A living systematic review

Background: The Zika virus (ZIKV) caused a large outbreak in the Americas leading to the declaration of a Public Health Emergency of International Concern in February 2016. A causal relation between infection and adverse congenital outcomes such as microcephaly was declared by the World Health Organization (WHO) informed by a systematic review structured according to a framework of ten dimensions of causality, based on the work of Bradford Hill. Subsequently, the evidence has continued to accumulate, which we incorporate in regular updates of the original work, rendering it a living systematic review.

Methods: We present an update of our living systematic review on the causal relation between ZIKV infection and adverse congenital outcomes and between ZIKV and GBS for four dimensions of causality: strength of association, dose-response, specificity, and consistency. We assess the evidence published between January 18, 2017 and July 1, 2019.

Results: We found that the strength of association between ZIKV infection and adverse outcomes from case-control studies differs according to whether exposure to ZIKV is assessed in the mother (OR 3.8, 95% CI: 1.7-8.7, I ²=19.8%) or the foetus/infant (OR 37.4, 95% CI: 11.0-127.1, I ²=0%). In cohort studies, the risk of congenital abnormalities was 3.5 times higher after ZIKV infection (95% CI: 0.9-13.5, I ²=0%). The strength of association between ZIKV infection and GBS was higher in studies that enrolled controls from hospital (OR: 55.8, 95% CI: 17.2-181.7, I ²=0%) than in studies that enrolled controls at random from the same community or household (OR: 2.0, 95% CI: 0.8-5.4, I ²=74.6%). In case-control studies, selection of controls from hospitals could have biased results.

Conclusions: The conclusions that ZIKV infection causes adverse congenital outcomes and GBS are reinforced with the evidence published between January 18, 2017 and July 1, 2019.

An assessment of public health surveillance of Zika virus infection and potentially associated outcomes in Latin America

BACKGROUND

We evaluated whether outbreaks of Zika virus (ZIKV) infection, newborn microcephaly, and Guillain-Barré syndrome (GBS) in Latin America may be detected through current surveillance systems, and how cases detected through surveillance may increase health care burden.

METHODS

We estimated the sensitivity and specificity of surveillance case definitions using published data. We assumed a 10% ZIKV infection risk during a non-outbreak period and hypothetical increases in risk during an outbreak period. We used sensitivity and specificity estimates to correct for non-differential misclassification, and calculated a misclassification-corrected relative risk comparing both periods. To identify the smallest hypothetical increase in risk resulting in a detectable outbreak we compared the misclassification-corrected relative risk to the relative risk corresponding to the upper limit of the endemic channel (mean + 2 SD). We also estimated the proportion of false positive cases detected during the outbreak. We followed the same approach for microcephaly and GBS, but assumed the risk of ZIKV infection doubled during the outbreak, and ZIKV infection increased the risk of both diseases.

RESULTS

ZIKV infection outbreaks were not detectable through non-serological surveillance. Outbreaks were detectable through serologic surveillance if infection risk increased by at least 10%, but more than 50% of all cases were false positive. Outbreaks of severe microcephaly were detected if ZIKV infection increased prevalence of this condition by at least 24.0 times. When ZIKV infection did not increase the prevalence of severe microcephaly, 34.7 to 82.5% of all cases were false positive, depending on diagnostic accuracy. GBS outbreaks were detected if ZIKV infection increased the GBS risk by at least seven times. For optimal GBS diagnosis accuracy, the proportion of false positive cases ranged from 29 to 54% and from 45 to 56% depending on the incidence of GBS mimics.

CONCLUSIONS

Current surveillance systems have a low probability of detecting outbreaks of ZIKV infection, severe microcephaly, and GBS, and could result in significant increases in health care burden, due to the detection of large numbers of false positive cases. In view of these limitations, Latin American countries should consider alternative options for surveillance.