Change in global transmission rates of COVID-19 through May 6 2020.

We analyzed COVID-19 data through May 6th, 2020 using a partially observed Markov process. Our method uses a hybrid deterministic and stochastic formalism that allows for time variable transmission rates and detection probabilities. The model was fit using iterated particle filtering to case count and death count time series from 55 countries. We found evidence for a shrinking epidemic in 30 of the 55 examined countries. Of those 30 countries, 27 have significant evidence for subcritical transmission rates, although the decline in new cases is relatively slow compared to the initial growth rates. Generally, the transmission rates in Europe were lower than in the Americas and Asia. This suggests that global scale social distancing efforts to slow the spread of COVID-19 are effective although they need to be strengthened in many regions and maintained in others to avoid further resurgence of COVID-19. The slow decline also suggests alternative strategies to control the virus are needed before social distancing efforts are partially relaxed.

Correction: Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention.

[This corrects the article DOI: 10.1371/journal.pone.0204741.].

Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention.

Predicting the population-level effects of an infectious disease intervention that incorporate multiple modes of intervention is complicated by the joint non-linear dynamics of both infection transmission and the intervention itself. In this paper, we consider the sensitivity of Dynamic Optimal Control Profiles (DOCPs) for the optimal joint investment in both a contagiousness and susceptibility-based control of HIV to bio-behavioral, economic, and programmatic assumptions. The DOCP is calculated using recently developed numerical algorithms that allow controls to be represented by a set of piecewise constant functions that maintain a constant yearly budget. Our transmission model assumes multiple stages of HIV infection corresponding to acute and chronic infection and both within- and between-individual behavioral heterogeneity. We parameterize a baseline scenario from a longitudinal study of sexual behavior in MSM and consider sensitivity of the DOCPs to deviations from that baseline scenario. In the baseline scenario, the primary determinant of the dominant control were programmatic factors, regardless of budget. In sensitivity analyses, the qualitative aspects of the optimal control policy were often robust to significant deviation in assumptions regarding transmission dynamics. In addition, we found several conditions in which long-term joint investment in both interventions was optimal. Our results suggest that modeling in the service of decision support for intervention design can improve population-level effects of a limited set of economic resources. We found that economic and programmatic factors were as important as the inherent transmission dynamics in determining population-level intervention effects. Given our finding that the DOCPs were robust to alternative biological and behavioral assumptions it may be possible to identify DOCPs even when the data are not sufficient to identify a transmission model.

Screening for hepatitis D and PEG-Interferon over Tenofovir enhance general hepatitis control efforts in Brazil.

BACKGROUND: Hepatitis D virus (HDV), which requires the presence of hepatitis B virus (HBV), is a deadly yet neglected disease that rapidly leads to liver cancer and disease-induced mortality. This co-dependence creates complex transmission dynamics that make it difficult to predict the efficacy of interventions aimed at HBV and/or HDV control in endemic regions, such as certain municipalities of Brazil, where up to 65% of HBV-infected persons are co-infected. METHODOLOGY: We created a mathematical model that captures the joint transmission dynamics of HBV and HDV, incorporating mother-to-child, sexual and household transmission. With an aim to minimize the number of total infections and disease-induced mortality in 2027, we then determined optimal strategies for Brazil and its sub-regions under a constrained budget, which was dynamically allocated among HBV and HDV screening, HBV and HDV treatment, HBV newborn and adult vaccination, and awareness programs. Three treatment options were considered, namely: Tenofovir, PEGylated-Interferon, and nucleic acid polymers (NAP). RESULTS: The additional cost of HDV screening and the use of a more expensive PEGylated-Interferon are offset by not wasting resources on treating co-infected persons with Tenofovir. The introductory price of NAP treatment must be less than $16,000 per course to become competitive with Tenofovir and PEGylated-Interferon in Brazil. CONCLUSION: Additional screening for HDV is beneficial, even in a low HBV and HDV endemic regions of Brazil. We recommend PEGylated-Interferon, wherever possible, for both HBV and HDV. If PEGylated-Interferon is not available in abundance, PEGylated-Interferon for co-infections and 4-year Tenofovir treatment for mono-infections is recommended.

Inference of Transmission Network Structure from HIV Phylogenetic Trees.

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

Trends of HIV-1 incidence with credible intervals in Sweden 2002-09 reconstructed using a dynamic model of within-patient IgG growth.

BACKGROUND: HIV-1 is a lifelong disease, often without serious symptoms for years after infection, and thus many infected persons go undetected for a long time. This makes it difficult to track incidence, and thus epidemics may go through dramatic changes largely unnoticed, only to be detected years later. Because direct measurement of incidence is expensive and difficult, several biomarker-based tests and algorithms have been developed to distinguish between recent and long-term infections. However, current methods have been criticized and demands for novel methods have been raised. METHODS: We developed and applied a biomarker-based incidence model, joining a time-continuous model of immunoglobulin G (IgG) growth (measured by the IgG-capture BED-enzyme immunoassay) with statistical corrections for both sample size and unobserved diagnoses. Our method uses measurements of IgG concentration in newly diagnosed people to calculate the posterior distribution of infection times. Time from infection to diagnosis is modelled for all individuals in a given period and is used to calculate a sample weight to correct for undiagnosed individuals. We then used a bootstrapping method to reconstruct point estimates and credible intervals of the incidence of HIV-1 in Sweden based on a sample of newly diagnosed people. RESULTS: We found evidence for: (i) a slowly but steadily increasing trend in both the incidence and incidence rate in Sweden; and (ii) an increasing but well-controlled epidemic in gay men in Stockholm. Sensitivity analyses showed that our method was robust to realistic levels (up to 15%) of BED misclassification of non-recently infected persons as early infections. CONCLUSIONS: We developed a novel incidence estimator based on previously published theoretical work that has the potential to provide rapid, up-to-date estimates of HIV-1 incidence in populations where BED test data are available.

Acute-stage transmission of HIV: effect of volatile contact rates.

BACKGROUND: The role of acute-stage transmission in sustaining HIV epidemics has been difficult to determine. This difficulty is exacerbated by a lack of theoretical understanding of how partnership dynamics and sexual behavior interact to affect acute-stage transmission. We propose that individual-level variation in rates of sexual contact is a key aspect of partnership dynamics that can greatly increase acute-stage HIV transmission. METHODS: Using an individual-based stochastic framework, we simulated a model of HIV transmission that includes individual-level changes in contact rates. We report both population-level statistics (such as prevalence and acute-stage transmission rates) and individual-level statistics (such as the contact rate at the time of infection). RESULTS: Volatility increases both the prevalence of HIV and the proportion of new cases from acute-stage infectors. These effects result from 1) a relative reduction in transmission rate from chronic but not acute infectors and 2) an increase in the availability of high-risk susceptibles. CONCLUSIONS: The extent of changes in individual-level contact rates in the real world is unknown. Aggregate or strictly cross-sectional data do not reveal individual-level changes in partnership dynamics and sexual behavior. The strong effects presented in this article motivate both continued theoretical exploration of volatility in sexual behavior and collection of longitudinal individual-level data to inform more realistic models.

Detectable signals of episodic risk effects on acute HIV transmission: strategies for analyzing transmission systems using genetic data.

Episodic high-risk sexual behavior is common and can have a profound effect on HIV transmission. In a model of HIV transmission among men who have sex with men (MSM), changing the frequency, duration and contact rates of high-risk episodes can take endemic prevalence from zero to 50% and more than double transmissions during acute HIV infection (AHI). Undirected test and treat could be inefficient in the presence of strong episodic risk effects. Partner services approaches that use a variety of control options will be likely to have better effects under these conditions, but the question remains: What data will reveal if a population is experiencing episodic risk effects? HIV sequence data from Montreal reveals genetic clusters whose size distribution stabilizes over time and reflects the size distribution of acute infection outbreaks (AIOs). Surveillance provides complementary behavioral data. In order to use both types of data efficiently, it is essential to examine aspects of models that affect both the episodic risk effects and the shape of transmission trees. As a demonstration, we use a deterministic compartmental model of episodic risk to explore the determinants of the fraction of transmissions during acute HIV infection (AHI) at the endemic equilibrium. We use a corresponding individual-based model to observe AIO size distributions and patterns of transmission within AIO. Episodic risk parameters determining whether AHI transmission trees had longer chains, more clustered transmissions from single individuals, or different mixes of these were explored. Encouragingly for parameter estimation, AIO size distributions reflected the frequency of transmissions from acute infection across divergent parameter sets. Our results show that episodic risk dynamics influence both the size and duration of acute infection outbreaks, thus providing a possible link between genetic cluster size distributions and episodic risk dynamics.