An HIV Epidemic Model Based on Viral Load Dynamics: Value in Assessing Empirical Trends in HIV Virulence and Community Viral Load
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{"title"=>"An HIV Epidemic Model Based on Viral Load Dynamics: Value in Assessing Empirical Trends in HIV Virulence and Community Viral Load", "type"=>"journal", "authors"=>[{"first_name"=>"Joshua T.", "last_name"=>"Herbeck", "scopus_author_id"=>"8270002100"}, {"first_name"=>"John E.", "last_name"=>"Mittler", "scopus_author_id"=>"7005159402"}, {"first_name"=>"Geoffrey S.", "last_name"=>"Gottlieb", "scopus_author_id"=>"7102754246"}, {"first_name"=>"James I.", "last_name"=>"Mullins", "scopus_author_id"=>"35397850800"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"373401406", "isbn"=>"1553-7358 (Electronic) 1553-734X (Linking)", "issn"=>"15537358", "doi"=>"10.1371/journal.pcbi.1003673", "scopus"=>"2-s2.0-84903386044", "pmid"=>"24945322", "sgr"=>"84903386044"}, "id"=>"597f7291-8a53-3d3a-ad88-653b82158bc6", "abstract"=>"Trends in HIV virulence have been monitored since the start of the AIDS pandemic, as studying HIV virulence informs our understanding of HIV epidemiology and pathogenesis. Here, we model changes in HIV virulence as a strictly evolutionary process, using set point viral load (SPVL) as a proxy, to make inferences about empirical SPVL trends from longitudinal HIV cohorts. We develop an agent-based epidemic model based on HIV viral load dynamics. The model contains functions for viral load and transmission, SPVL and disease progression, viral load trajectories in multiple stages of infection, and the heritability of SPVL across transmissions. We find that HIV virulence evolves to an intermediate level that balances infectiousness with longer infected lifespans, resulting in an optimal SPVL∼4.75 log10 viral RNA copies/mL. Adaptive viral evolution may explain observed HIV virulence trends: our model produces SPVL trends with magnitudes that are broadly similar to empirical trends. With regard to variation among studies in empirical SPVL trends, results from our model suggest that variation may be explained by the specific epidemic context, e.g. the mean SPVL of the founding lineage or the age of the epidemic; or improvements in HIV screening and diagnosis that results in sampling biases. We also use our model to examine trends in community viral load, a population-level measure of HIV viral load that is thought to reflect a population's overall transmission potential. We find that community viral load evolves in association with SPVL, in the absence of prevention programs such as antiretroviral therapy, and that the mean community viral load is not necessarily a strong predictor of HIV incidence.", "link"=>"http://www.mendeley.com/research/hiv-epidemic-model-based-viral-load-dynamics-value-assessing-empirical-trends-hiv-virulence-communit", "reader_count"=>55, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>4, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>4, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>14, "Business, Management and Accounting"=>1, "Chemical Engineering"=>1, "Computer Science"=>2, "Engineering"=>1, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Nursing and Health Professions"=>1, "Mathematics"=>8, "Medicine and Dentistry"=>11, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Social Sciences"=>1, "Immunology and Microbiology"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>11}, "Social Sciences"=>{"Social Sciences"=>1}, "Mathematics"=>{"Mathematics"=>8}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Engineering"=>{"Engineering"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"United States"=>4, "United Kingdom"=>1, "Germany"=>1, "India"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1540420"], "description"=>"<p>Distributions of linear SPVL trends (log<sub>10</sub> HIV RNA copies/mL/year) were estimated from 100 randomly sampled 20-year time periods for 10 replicate simulations for each initial mean SPVL = 3.5, 4.5 or 5.5 log<sub>10</sub> HIV RNA copies/mL (creating 1000 total 20-year trends for each initial mean SPVL). Empirical (published) annual linear SPVL trends are overlaid (arrows and references). References with asterisks are seroprevalent cohorts; all others are seroconverter cohorts. <b>B. </b><b>Selection of an appropriate null changes the distribution of simulated SPVL trends.</b> Separate null distributions, each spanning a different subset of the complete 100-year simulated epidemics: all 100 years of the model output; years 10–100 of the model output, as European and North American subtype B epidemics began ∼1970, and studies of empirical SPVL trends began sampling at the earliest in 1984, leaving a ∼10-year window of the HIV epidemic not sampled by the cohorts; years 0–40 of the model output, as the empirical studies of SPVL trends include years up to ∼2010, so this represents the first 40 years of the subtype B epidemic (∼1970 to 2010); and years 10–40, reflecting the empirical sampling years ∼1980 to 2010.</p>", "links"=>[], "tags"=>["Evolutionary biology", "Evolutionary processes", "Evolutionary adaptation", "natural selection", "Organismal evolution", "Microbial evolution", "Viral evolution", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "Virology", "Viral transmission and infection", "Viral load", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "Pathology and laboratory medicine", "pathogenesis", "Host-pathogen interactions", "empirical", "spvl", "trends", "overlaid", "distributions", "simulated", "20-year"], "article_id"=>1062174, "categories"=>["Biological Sciences"], "users"=>["Joshua T. Herbeck", "John E. Mittler", "Geoffrey S. Gottlieb", "James I. Mullins"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003673.g002", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Empirical_SPVL_trends_overlaid_onto_distributions_of_simulated_20_year_trends_/1062174", "title"=>"A. Empirical SPVL trends overlaid onto distributions of simulated 20-year trends.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-19 02:54:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1540428"], "description"=>"<p>Parameters of the model and standard initial values.</p>", "links"=>[], "tags"=>["Evolutionary biology", "Evolutionary processes", "Evolutionary adaptation", "natural selection", "Organismal evolution", "Microbial evolution", "Viral evolution", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "Virology", "Viral transmission and infection", "Viral load", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "Pathology and laboratory medicine", "pathogenesis", "Host-pathogen interactions"], "article_id"=>1062183, "categories"=>["Biological Sciences"], "users"=>["Joshua T. Herbeck", "John E. Mittler", "Geoffrey S. Gottlieb", "James I. Mullins"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003673.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_of_the_model_and_standard_initial_values_/1062183", "title"=>"Parameters of the model and standard initial values.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-19 02:54:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1540427"], "description"=>"<p><b>A.</b> Plot of yearly estimates of mean community viral load versus annual incidence, for 100 years of a simulated epidemic. <b>B.</b> Distributions of <i>P</i>-values for Spearman correlations between mean community viral load and incidence, by year, for 10-year periods from the same 100 year epidemic, from a sliding window of 10 with one year increments. Shown are a plot of CVL and incidence for a 100-year simulated epidemic, Spearman correlation coefficients between CVL and incidence for each overlapping 10-year period, and <i>P</i>-value for each Spearman correlation test. Significant associations between CVL and incidence can be positive or negative, depending on epidemic context.</p>", "links"=>[], "tags"=>["Evolutionary biology", "Evolutionary processes", "Evolutionary adaptation", "natural selection", "Organismal evolution", "Microbial evolution", "Viral evolution", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "Virology", "Viral transmission and infection", "Viral load", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "Pathology and laboratory medicine", "pathogenesis", "Host-pathogen interactions", "viral", "linearly"], "article_id"=>1062182, "categories"=>["Biological Sciences"], "users"=>["Joshua T. Herbeck", "John E. Mittler", "Geoffrey S. Gottlieb", "James I. Mullins"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003673.g005", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_community_viral_load_is_not_linearly_or_consistently_associated_with_annual_incidence_/1062182", "title"=>"Mean community viral load is not linearly or consistently associated with annual incidence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-19 02:54:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1540436", "https://ndownloader.figshare.com/files/1540437", "https://ndownloader.figshare.com/files/1540438", "https://ndownloader.figshare.com/files/1540439", "https://ndownloader.figshare.com/files/1540440", "https://ndownloader.figshare.com/files/1540441", "https://ndownloader.figshare.com/files/1540442", "https://ndownloader.figshare.com/files/1540443", "https://ndownloader.figshare.com/files/1540444", "https://ndownloader.figshare.com/files/1540445"], "description"=>"<div><p>Trends in HIV virulence have been monitored since the start of the AIDS pandemic, as studying HIV virulence informs our understanding of HIV epidemiology and pathogenesis. Here, we model changes in HIV virulence as a strictly evolutionary process, using <i>set point viral load</i> (SPVL) as a proxy, to make inferences about empirical SPVL trends from longitudinal HIV cohorts. We develop an agent-based epidemic model based on HIV viral load dynamics. The model contains functions for viral load and transmission, SPVL and disease progression, viral load trajectories in multiple stages of infection, and the heritability of SPVL across transmissions. We find that HIV virulence evolves to an intermediate level that balances infectiousness with longer infected lifespans, resulting in an optimal SPVL∼4.75 log<sub>10</sub> viral RNA copies/mL. Adaptive viral evolution may explain observed HIV virulence trends: our model produces SPVL trends with magnitudes that are broadly similar to empirical trends. With regard to variation among studies in empirical SPVL trends, results from our model suggest that variation may be explained by the specific epidemic context, <i>e.g.</i> the mean SPVL of the founding lineage or the age of the epidemic; or improvements in HIV screening and diagnosis that results in sampling biases. We also use our model to examine trends in community viral load, a population-level measure of HIV viral load that is thought to reflect a population's overall transmission potential. We find that community viral load evolves in association with SPVL, in the absence of prevention programs such as antiretroviral therapy, and that the mean community viral load is not necessarily a strong predictor of HIV incidence.</p></div>", "links"=>[], "tags"=>["Evolutionary biology", "Evolutionary processes", "Evolutionary adaptation", "natural selection", "Organismal evolution", "Microbial evolution", "Viral evolution", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "Virology", "Viral transmission and infection", "Viral load", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "Pathology and laboratory medicine", "pathogenesis", "Host-pathogen interactions", "hiv", "viral", "empirical", "trends", "virulence"], "article_id"=>1062191, "categories"=>["Biological Sciences"], "users"=>["Joshua T. Herbeck", "John E. Mittler", "Geoffrey S. Gottlieb", "James I. Mullins"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003673.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003673.s010"], "stats"=>{"downloads"=>12, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_HIV_Epidemic_Model_Based_on_Viral_Load_Dynamics_Value_in_Assessing_Empirical_Trends_in_HIV_Virulence_and_Community_Viral_Load_/1062191", "title"=>"An HIV Epidemic Model Based on Viral Load Dynamics: Value in Assessing Empirical Trends in HIV Virulence and Community Viral Load", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-06-19 02:54:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1540417"], "description"=>"<p><b>A.</b> Epidemic size over time. Epidemic runs with each initial SPVL were repeated 10 times, each run with a different random number seed. <b>B.</b> Population mean set point viral load (SPVL; log<sub>10</sub> HIV RNA copies/mL at the end of acute infection) over time, using a locally weighted polynomial regression curve (Lowess fit = 0.1). Mean SPVL evolves toward 4.75 log<sub>10</sub> copies/mL.</p>", "links"=>[], "tags"=>["Evolutionary biology", "Evolutionary processes", "Evolutionary adaptation", "natural selection", "Organismal evolution", "Microbial evolution", "Viral evolution", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "Virology", "Viral transmission and infection", "Viral load", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "Pathology and laboratory medicine", "pathogenesis", "Host-pathogen interactions", "replicate", "simulated"], "article_id"=>1062172, "categories"=>["Biological Sciences"], "users"=>["Joshua T. Herbeck", "John E. Mittler", "Geoffrey S. Gottlieb", "James I. Mullins"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003673.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variation_across_replicate_simulated_epidemics_/1062172", "title"=>"Variation across replicate simulated epidemics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-19 02:54:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1540425"], "description"=>"<p>More extreme SPVL trends occur very early in simulated epidemics (the first 20 years). Boxplots of linear SPVL trends estimated from 100 randomly sampled 20-year time periods; thick line = median; box edges = quartiles; whiskers = minimum and maximum trends. <b>B. </b><b>Trends in mean community viral load and mean set point viral load are related.</b> Mean community viral load can evolve over time in the absence of HIV prevention programs. Community viral loads are estimated for each day using viral load measurements from each infected and alive individual, except for those individuals who have been infected less than 45 days (acute infection lasts for 3 months days in these simulations).</p>", "links"=>[], "tags"=>["Evolutionary biology", "Evolutionary processes", "Evolutionary adaptation", "natural selection", "Organismal evolution", "Microbial evolution", "Viral evolution", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "Virology", "Viral transmission and infection", "Viral load", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "Pathology and laboratory medicine", "pathogenesis", "Host-pathogen interactions", "distributions", "spvl", "trends", "simulated", "epidemics"], "article_id"=>1062180, "categories"=>["Biological Sciences"], "users"=>["Joshua T. Herbeck", "John E. Mittler", "Geoffrey S. Gottlieb", "James I. Mullins"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003673.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Distributions_of_SPVL_trends_change_as_simulated_epidemics_progress_/1062180", "title"=>"A. Distributions of SPVL trends change as simulated epidemics progress.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-19 02:54:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1540422"], "description"=>"<p><b>A.</b> Comparison of distribution of 20-year linear SPVL trends estimated from unbiased (black lines, initial mean SPVL = 3.5; grey lines, initial mean SPVL = 4.5) and biased (dotted lines, multiple colors representing multiple sub-sampling levels) data sets. The underlying distributions are produced from years 0 to 100 from simulated epidemics. Removing subsets (%) of all individuals (a schematic representation of the biased sampling process is shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003673#pcbi.1003673.s001\" target=\"_blank\">Figure S1A</a>) results in a distribution of linear trends with a median SPVL trend of greater magnitude than the unbiased trends. <b>B.</b> Comparison of distribution of 20-year linear SPVL trends estimated from unbiased and biased data sets, but with the underlying distributions produced from years 10 to 40 from simulated epidemics.</p>", "links"=>[], "tags"=>["Evolutionary biology", "Evolutionary processes", "Evolutionary adaptation", "natural selection", "Organismal evolution", "Microbial evolution", "Viral evolution", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "Virology", "Viral transmission and infection", "Viral load", "Population biology", "Population Dynamics", "epidemiology", "Disease dynamics", "Infectious disease epidemiology", "Pathology and laboratory medicine", "pathogenesis", "Host-pathogen interactions", "sampling", "biases", "estimation", "model-based", "spvl"], "article_id"=>1062177, "categories"=>["Biological Sciences"], "users"=>["Joshua T. Herbeck", "John E. Mittler", "Geoffrey S. Gottlieb", "James I. Mullins"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003673.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_sampling_biases_on_the_estimation_of_model_based_SPVL_trends_/1062177", "title"=>"The effect of sampling biases on the estimation of model-based SPVL trends.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-19 02:54:47"}

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[291]}, {"subject_area"=>"/Medicine and health sciences/Diagnostic medicine", "average_usage"=>[257]}, {"subject_area"=>"/Medicine and health sciences/Infectious diseases", "average_usage"=>[319]}]}
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