Insights into intercontinental spread of Zika virus

<div><p>The epidemic of Zika virus (ZIKV) infection in South America has led to World Health Organization’s declaration of a Public Health Emergency of International Concern. To further inform effective public health policy, an understanding of ZIKV’s transmission mechanisms is crucial. To characterize the intercontinental transmission of ZIKV, we compiled and analyzed more than 250 gene sequences together with their sequence-related geographic and temporal information, sampled across 27 countries spanning from 1947 to 2016. After filtering and selecting appropriate sequences, extensive phylogenetic analyses were performed. Although phylogeographic reconstruction supported the transmission route of the virus in Africa, South-eastern Asia, Oceania and Latin America, we discovered that the Eastern Africa origin of ZIKV was disputable. On a molecular level, purifying selection was found to be largely responsible for the evolution of non-structural protein 5 and envelope protein E. Our dataset and ancestral sequences reconstruction analysis captured previously unidentified amino acid changes during evolution. Finally, based on the estimation of the time to the most recent common ancestors for the non-structural protein 5 gene, we hypothesized potential specific historic events that occurred in the 1940s and might have facilitated the spread of Zika virus from Africa to South-eastern Asia. Our findings provide new insights into the transmission characteristics of ZIKV, while further genetic and serologic studies are warranted to support the design of tailored prevention strategies.</p></div

Result of analyses on amino acid changes.

<p>Result of analyses on amino acid changes.</p

Zika virus (ZIKV) population dynamics of genetic diversity over time.

<p>The effective estimated population size of virus is shown on y-axis. X-axis shows the time before 2016. The colored area corresponds to the credibility interval based on 95% highest HPD. Mean and median values for relative genetic diversity (y-axis) together with credibility intervals were plotted through time (x-axis). (A) NS5 by African lineage (B) NS5 by South Pacific Rim lineage (C) NS5 (D) ENV (E) NS1 (F) NS3.</p

Distribution of education and age of respondents by Body Image Type (BIT).

<p>*Abbreviations: <i>Bear</i> (B), <i>Chubby</i> (C), <i>Slender</i> (S), <i>Lean toned</i> (L), <i>Muscular</i> (M), <i>Average</i> (A) and <i>Others</i> (O).</p><p>Distribution of education and age of respondents by Body Image Type (BIT).</p

Estimation of the Undiagnosed Intervals of HIV-Infected Individuals by a Modified Back-Calculation Method for Reconstructing the Epidemic Curves

<div><p>Background</p><p>Undiagnosed infections accounted for the hidden proportion of HIV cases that have escaped from public health surveillance. To assess the population risk of HIV transmission, we estimated the undiagnosed interval of each known infection for constructing the HIV incidence curves.</p><p>Methods</p><p>We used modified back-calculation methods to estimate the seroconversion year for each diagnosed patient attending any one of the 3 HIV specialist clinics in Hong Kong. Three approaches were used, depending on the adequacy of CD4 data: (A) estimating one’s pre-treatment CD4 depletion rate in multilevel model;(B) projecting one’s seroconversion year by referencing seroconverters’ CD4 depletion rate; or (C) projecting from the distribution of estimated undiagnosed intervals in (B). Factors associated with long undiagnosed interval (>2 years) were examined in univariate analyses. Epidemic curves constructed from estimated seroconversion data were evaluated by modes of transmission.</p><p>Results</p><p>Between 1991 and 2010, a total of 3695 adult HIV patients were diagnosed. The undiagnosed intervals were derived from method (A) (28%), (B) (61%) and (C) (11%) respectively. The intervals ranged from 0 to 10 years, and were shortened from 2001. Heterosexual infection, female, Chinese and age >64 at diagnosis were associated with long undiagnosed interval. Overall, the peaks of the new incidence curves were reached 4–6 years ahead of reported diagnoses, while their contours varied by mode of transmission. Characteristically, the epidemic growth of heterosexual male and female declined after 1998 with slight rebound in 2004–2006, but that of MSM continued to rise after 1998.</p><p>Conclusions</p><p>By determining the time of seroconversion, HIV epidemic curves could be reconstructed from clinical data to better illustrate the trends of new infections. With the increasing coverage of antiretroviral therapy, the undiagnosed interval can add to the measures for assessing HIV transmission risk in the population.</p></div

Pre-treatment CD4 depletion rate in cells/month estimated in the linear multilevel models for patients in Group A and Group B.

<p>Pre-treatment CD4 depletion rate in cells/month estimated in the linear multilevel models for patients in Group A and Group B.</p

Summary for median of intervals between HIV diagnosis year and 3^rd quartile of the simulation results of seroconversion year in Group B patients.

<p>Summary for median of intervals between HIV diagnosis year and 3^rd quartile of the simulation results of seroconversion year in Group B patients.</p

Sensitivity analysis of new infection curves constructed by computing the first quartile, third quartile and median of the simulation results in Group A and B, and the median of undiagnosed interval in reference group (Group A, Group B, Group A and B) for Group C, by mode of transmission.

<p>Sensitivity analysis of new infection curves constructed by computing the first quartile, third quartile and median of the simulation results in Group A and B, and the median of undiagnosed interval in reference group (Group A, Group B, Group A and B) for Group C, by mode of transmission.</p

Characteristics of HIV-infected patients with long (>2 years) undiagnosed interval from estimated seroconversion to HIV diagnosis, compared to patients with a shorter interval.

<p>Characteristics of HIV-infected patients with long (>2 years) undiagnosed interval from estimated seroconversion to HIV diagnosis, compared to patients with a shorter interval.</p

HCV genotypes, by HIV status, among 96 drug users in Liuzhou cohort.

<p>HCV genotypes, by HIV status, among 96 drug users in Liuzhou cohort.</p