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Serosurveys are a key resource for measuring SARS-CoV-2 population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a post-infection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from five large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identify substantial differences between raw seroprevalence and cumulative incidence of over two-fold in the results of some surveys, and provide a tool for practitioners to generate cumulative incidence estimates with pre-set or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

serosimis an open source R package designed to aid inference of serological surveys, by simulating data arising from user-specified vaccine and infection-generated antibody kinetics processes using a random effects model. Serological surveys are used to assess population immunity by directly measuring individuals’ antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological surveys and new analytical techniques used to interpret them are increasingly widespread. This expansion creates a need for tools to simulate serological surveys and the processes underlying the observed titer values, as this will enable researchers to identify best practices for serological survey design, and provide a standardized framework to evaluate the performance of different inference methods.serosimallows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological survey sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological surveys, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.Author SummaryPublic health researchers use serological surveys to obtain serum samples from individuals and measure antibody levels against one or more pathogens. When paired with appropriate analytical methods, these surveys can be used to determine whether individuals have been previously infected with or vaccinated against those pathogens. However, there is currently a lack of tools to simulate realistic serological survey data from the processes determining these observed antibody levels. We developedserosim, an open source R package which enables users to simulate serological survey data matching their disease system(s) of interest. This package allows users to specify and modify model inputs responsible for generating an individual’s antibody level at various levels, from the within-host processes to the observation process.serosimwill be useful for designing more informative serological surveys, better understanding the processes behind observed serological data, and assessing new serological survey analytical methods.

Natural killer (NK) cells likely play an important role in immunity to malaria, but the effect of repeated malaria on NK cell responses remains unclear. Here, we comprehensively profiled the NK cell response in a cohort of 264 Ugandan children. Repeated malaria exposure was associated with expansion of an atypical, CD56 neg population of NK cells that differed transcriptionally, epigenetically, and phenotypically from CD56 dim NK cells, including decreased expression of PLZF and the Fc receptor γ-chain, increased histone methylation, and increased protein expression of LAG-3, KIR, and LILRB1. CD56 neg NK cells were highly functional and displayed greater antibody-dependent cellular cytotoxicity than CD56 dim NK cells. Higher frequencies of CD56 neg NK cells were associated with protection against symptomatic malaria and high parasite densities. After marked reductions in malaria transmission, frequencies of these cells rapidly declined, suggesting that continuous exposure to Plasmodium falciparum is required to maintain this modified, adaptive-like NK cell subset.

Interferon (IFN)–specific autoantibodies have been implicated in severe coronavirus disease 2019 (COVID-19) and have been proposed as a potential driver of the persistent symptoms characterizing “long COVID,” a type of postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We report that only 2 of 215 participants with convalescent SARS-CoV-2 infection tested over 394 time points, including 121 people experiencing long COVID symptoms, had detectable IFN-α2 antibodies. Both had been hospitalized during the acute phase of the infection. These data suggest that persistent anti-IFN antibodies, although a potential driver of severe COVID-19, are unlikely to contribute to long COVID symptoms in the postacute phase of the infection.

Natural Killer (NK) cells likely play an important role in immunity to malaria, but whether repeated malaria modifies the NK cell response remains unclear. Here, we comprehensively profiled the NK cell response in a cohort of 264 Ugandan children. Repeated malaria exposure was associated with expansion of an atypical, CD56neg population of NK cells that differed transcriptionally, epigenetically, and phenotypically from CD56dim NK cells, including decreased expression of PLZF and the Fc receptor g chain, increased histone methylation, and increased protein expression of LAG-3, KIR and LILRB1. CD56neg NK cells were highly functional, displaying greater antibody dependent cellular cytotoxicity than CD56dim NK cells, and higher frequencies of these cells were associated with protection against symptomatic malaria and high parasite densities. Importantly, following marked reductions in malaria transmission, frequencies of these cells rapidly declined, suggesting that continuous exposure to malaria is required to maintain this modified, adaptive-like NK cell subset.

The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013–2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide., Global change, including climate change, urbanization and global travel and trade, has affected the emergence and spread of infectious diseases. In the Review, Baker, Metcalf and colleagues examine how global change affects infectious diseases, highlighting examples ranging from COVID-19 to Zika virus disease.

ImportanceEstimating the true burden of SARS-CoV-2 infection has been difficult in sub-Saharan Africa due to asymptomatic infections and inadequate testing capacity. Antibody responses from serologic surveys can provide an estimate of SARS-CoV-2 exposure at the population level.ObjectiveTo estimate SARS-CoV-2 seroprevalence, attack rates, and re-infection in eastern Uganda using serologic surveillance from 2020 to early 2022.DesignPlasma samples from participants in the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria in Uganda (PRISM) Border Cohort were obtained at four sampling intervals: October-November 2020; March-April 2021; August-September 2021; and February-March 2022. Setting: Tororo and Busia districts, Uganda.Participants1,483 samples from 441 participants living in 76 households were tested. Each participant contributed up to 4 time points for SARS-CoV-2 serology, with almost half of all participants contributing at all 4 time points, and almost 90% contributing at 3 or 4 time points. Information on SARS-CoV-2 vaccination status was collected from participants, with the earliest reported vaccinations in the cohort occurring in May 2021.Main Outcome(s) and Measure(s)The main outcomes of this study were antibody responses to the SARS-CoV-2 spike protein as measured with a bead-based serologic assay. Individual-level outcomes were aggregated to population-level SARS-CoV-2 seroprevalence, attack rates, and boosting rates. Estimates were weighted by the local age distribution based on census data.ResultsBy the end of the Delta wave and before widespread vaccination, nearly 70% of the study population had experienced SARS-CoV-2 infection. During the subsequent Omicron wave, 85% of unvaccinated, previously seronegative individuals were infected for the first time, and ∼50% or more of unvaccinated, already seropositive individuals were likely re-infected, leading to an overall 96% seropositivity in this population. Our results suggest a lower probability of re-infection in individuals with higher pre-existing antibody levels. We found evidence of household clustering of SARS-CoV-2 seroconversion. We found no significant associations between SARS-CoV-2 seroconversion and gender, household size, or recent Plasmodium falciparum malaria exposure.Conclusions and RelevanceFindings from this study are consistent with very high infection rates and re-infection rates for SARS-CoV-2 in a rural population from eastern Uganda throughout the pandemic.

BackgroundCauses of non-malarial fevers in sub-Saharan Africa remain understudied. We hypothesized that metagenomic next-generation sequencing (mNGS), which allows for broad genomic-level detection of infectious agents in a biological sample, can systematically identify potential causes of non-malarial fevers.Methods and FindingsThe 212 participants in this study were of all ages and were enrolled in a longitudinal malaria cohort in eastern Uganda. Between December 2020 and August 2021, respiratory swabs and plasma samples were collected at 313 study visits where participants presented with fever and were negative for malaria by microscopy. Samples were analyzed using CZ ID, a web-based platform for microbial detection in mNGS data. Overall, viral pathogens were detected at 123 of 313 visits (39%). SARS-CoV-2 was detected at 11 visits, from which full viral genomes were recovered from nine. Other prevalent viruses included Influenza A (14 visits), RSV (12 visits), and three of the four strains of seasonal coronaviruses (6 visits). Notably, 11 influenza cases occurred between May and July 2021, coinciding with when the Delta variant of SARS-CoV-2 was circulating in this population. The primary limitation of this study is that we were unable to estimate the contribution of bacterial microbes to non-malarial fevers, due to the difficulty of distinguishing bacterial microbes that were pathogenic from those that were commensal or contaminants.ConclusionsThese results revealed the co-circulation of multiple viral pathogens likely associated with fever in the cohort during this time period. This study illustrates the utility of mNGS in elucidating the multiple causes of non-malarial febrile illness. A better understanding of the pathogen landscape in different settings and age groups could aid in informing diagnostics, case management, and public health surveillance systems.

BackgroundImplications of the COVID-19 pandemic for both populations and healthcare systems are vast. In addition to morbidity and mortality from COVID-19, the pandemic also has disrupted local health systems, including reductions or delays in routine vaccination services and catch-up vaccination campaigns that could lead to outbreaks of other infectious diseases that result in an additional burden of disease and strain on the healthcare system.Methods and FindingsWe evaluated the impact of the COVID-19 pandemic on Zambia’s routine childhood immunization program in 2020 using multiple sources of data. We relied on district-level administrative vaccination coverage data and Zambia’s 2018 Demographic and Health Survey to project disruptions to routine childhood vaccination within the pandemic year 2020 (N=5,670). Next, we leveraged serological data to predict age-specific measles seroprevalence and assessed the impact of changes in vaccination coverage on measles outbreak risk in each district. We found minor disruptions to routine administration of measles-rubella and pentavalent vaccines in 2020. This was in part due to Zambia’s Child Health Week held in June of 2020 which helped to reach children missed during the first six months of the year. We estimated that the two-month delay in a measles-rubella vaccination campaign, originally planned for September of 2020 but conducted in November of 2020 as a result of the pandemic, had little impact on modeled district-specific measles outbreak risks.ConclusionsThe pandemic only minimally increased the number of children missed by measles-rubella and pentavalent vaccines in 2020. However, the ongoing SARS-CoV-2 transmission since our analysis concluded means efforts to maintain routine immunization services and minimize the risk of measles outbreaks will continue to be critical. Fortunately, the methodological framework developed in this analysis relied on routinely collected data and can be used to evaluate COVID-19 pandemic disruptions in Zambia following 2020 and in other countries or for other vaccines at a sub-national level.

Serosurveillance provides a unique opportunity to quantify the proportion of the population that has been exposed to pathogens. Here, we developed and piloted Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), a platform through which we systematically tested remnant samples from routine blood draws in two major hospital networks in San Francisco for SARS-CoV-2 antibodies during the early months of the pandemic. Importantly, SCALE-IT allows for algorithmic sample selection and rich data on covariates by leveraging electronic health record data. We estimated overall seroprevalence at 4.2%, corresponding to a case ascertainment rate of only 4.9%, and identified important heterogeneities by neighborhood, homelessness status, and race/ethnicity. Neighborhood seroprevalence estimates from SCALE-IT were comparable to local community-based surveys, while providing results encompassing the entire city that have been previously unavailable. Leveraging this hybrid serosurveillance approach has strong potential for application beyond this local context and for diseases other than SARS-CoV-2., Population-based surveys are the gold standard for estimating seroprevalence but are expensive and often only capture a small geographic area or window of time. This study describes a new platform, SCALE-IT, for serosurveillance based on algorithmic sampling of electronic health records, and uses it to estimate the seroprevalence of SARS-CoV-2 in San Francisco.

Protection against Plasmodium falciparum, which is primarily antibody-mediated, requires recurrent exposure to develop. The study of both naturally acquired limited immunity and vaccine induced protection against malaria remains critical for ongoing eradication efforts. Towards this goal, we deployed a customized P. falciparum PhIP-seq T7 phage display library containing 238,068 tiled 62-amino acid peptides, covering all known coding regions, including antigenic variants, to systematically profile antibody targets in 198 Ugandan children and adults from high and moderate transmission settings. Repeat elements – short amino acid sequences repeated within a protein – were significantly enriched in antibody targets. While breadth of responses to repeat-containing peptides was twofold higher in children living in the high versus moderate exposure setting, no such differences were observed for peptides without repeats, suggesting that antibody responses to repeat-containing regions may be more exposure dependent and/or less durable in children than responses to regions without repeats. Additionally, short motifs associated with seroreactivity were extensively shared among hundreds of antigens, potentially representing cross- reactive epitopes. PfEMP1 shared motifs with the greatest number of other antigens, partly driven by the diversity of PfEMP1 sequences. These data suggest that the large number of repeat elements and potential cross-reactive epitopes found within antigenic regions of P. falciparum could contribute to the inefficient nature of malaria immunity.

Interferon (IFN)-specific autoantibodies have been implicated in severe COVID-19 and have been proposed as a potential driver of the persistent symptoms characterizing Long COVID, a type of post-acute sequelae of SARS-CoV-2 infection (PASC). We report than only two of 215 SARS-CoV-2 convalescent participants tested over 394 timepoints, including 121 people experiencing Long COVID symptoms, had detectable IFN-α2 antibodies. Both had been hospitalized during the acute phase of the infection. These data suggest that persistent anti-IFN antibodies, although a potential driver of severe COVID-19, are unlikely to contribute to Long COVID symptoms in the post-acute phase of the infection.

BackgroundAs COVID-19 vaccines continue to be rolled-out, the “double burden” of health disparities in both exposure to infection and vaccination coverage intersect to determine the current and future patterns of infection, immunity, and mortality. Serology provides a unique opportunity to measure biomarkers of infection and vaccination simultaneously, and to relate these metrics to demographic and geographic factors.MethodsLeveraging algorithmically selected residual serum samples from two hospital networks in San Francisco, we sampled 1014 individuals during February 2021, capturing transmission during the first 11 months of the epidemic and the early roll out of vaccination. These samples were tested using two serologic assays: one detecting antibodies elicited by infection, and not by vaccines, and one detecting antibodies elicited by both infection and vaccination. We used Bayesian statistical models to estimate the proportion of the population that was naturally infected and the proportion protected due to vaccination.FindingsWe estimated that the risk of prior infection of Latinx residents was 5.3 (95% CI: 3.2 - 10.3) times greater than the risk of white residents aged 18-64 and that white San Francisco residents over the age of 65 were twice as likely (2.0, 95% CI: 1.1 - 4.6) to be vaccinated as Black residents. We also found socioeconomically deprived zipcodes in the city had high probabilities of natural infections and lower vaccination coverage than wealthier zipcodes.InterpretationUsing a platform we created for SARS-CoV-2 serologic data collection in San Francisco, we characterized and quantified the stark disparities in infection rates and vaccine coverage by demographic groups over the first year of the pandemic. While the arrival of the SARS-CoV-2 vaccine has created a ‘light at the end of the tunnel’ for this pandemic, ongoing challenges in achieving and maintaining equity must also be considered.FundingNIH, NIGMS, Schmidt Science Fellows in partnership with the Rhodes Trust and the Chan Zuckerberg Biohub.

ImportanceEstimating the true burden of SARS-CoV-2 infection has been difficult in sub-Saharan Africa owing to asymptomatic infections and inadequate testing capacity. Antibody responses from serologic surveys can provide an estimate of SARS-CoV-2 exposure at the population level.ObjectiveTo estimate SARS-CoV-2 seroprevalence, attack rates, and reinfection in eastern Uganda using serologic surveillance from 2020 to early 2022.Design, Setting, and ParticipantsThis cohort study was conducted in the Tororo and Busia districts of eastern Uganda. Plasma samples from participants in the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria in Uganda Border Cohort were obtained at 4 sampling intervals: October to November 2020, March to April 2021, August to September 2021, and February to March 2022. Each participant contributed up to 4 time points for SARS-CoV-2 serology, with almost half of all participants contributing at all 4 time points, and almost 90% contributing at 3 or 4 time points. Information on SARS-CoV-2 vaccination status was collected from participants, with the earliest reported vaccinations in the cohort occurring in May 2021.Main Outcomes and MeasuresThe main outcomes of this study were antibody responses to the SARS-CoV-2 spike protein as measured with a bead-based serologic assay. Individual-level outcomes were aggregated to population-level SARS-CoV-2 seroprevalence, attack rates, and boosting rates. Estimates were weighted by the local age distribution according to census data.ResultsA total of 1483 samples from 441 participants living in 76 households were tested. Of the 441 participants, 245 (55.6%) were female, and their mean (SD) age was 16.04 (16.04) years. By the end of the Delta wave and before widespread vaccination, adjusted SARS-CoV-2 seroprevalence was 67.7% (95% credible interval [CrI], 62.5%-72.6%) in the study population. During the subsequent Omicron wave, 84.8% (95% CrI, 67.9%-93.7%) of unvaccinated, previously seronegative individuals were infected for the first time, and 50.8% (95% CrI, 40.6%-59.7%) of unvaccinated, already seropositive individuals were likely reinfected, leading to an overall seropositivity of 96.0% (95% CrI, 93.4%-97.9%) in this population. These results suggest a lower probability of reinfection in individuals with higher preexisting antibody levels. There was evidence of household clustering of SARS-CoV-2 seroconversion. No significant associations were found between SARS-CoV-2 seroconversion and gender, household size, or recent Plasmodium falciparum malaria exposure.Conclusions and RelevanceIn this cohort study in a rural population in eastern Uganda, there was evidence of very high SARS-CoV-2 infection rates throughout the pandemic inconsistent with national level case data and high reinfection rates during the Omicron wave.

The great majority of SARS-CoV-2 infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, mild to moderate infections are an important contributor to ongoing transmission. There remains a critical need to identify host immune biomarkers predictive of clinical and virologic outcomes in SARS-CoV-2-infected patients. Leveraging longitudinal samples and data from a clinical trial of Peginterferon Lambda for treatment of SARS-CoV-2 infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients within the first 2 weeks of symptom onset. We define early immune signatures, including plasma levels of RIG-I and the CCR2 ligands (MCP1, MCP2 and MCP3), associated with control of oropharyngeal viral load, the degree of symptom severity, and immune memory (including SARS-CoV-2-specific T cell responses and spike (S) protein-binding IgG levels). We found that individuals receiving BNT162b2 (Pfizer–BioNTech) vaccine had similar early immune trajectories to those observed in this natural infection cohort, including the induction of both inflammatory cytokines (e.g. MCP1) and negative immune regulators (e.g. TWEAK). Finally, we demonstrate that machine learning models using 8-10 plasma protein markers measured early within the course of infection are able to accurately predict symptom severity, T cell memory, and the antibody response post-infection.

Using rats, we previously found that vitamin C deficiency increases serum levels of interleukin-6 (IL-6) and glucocorticoid, and changes the gene expression of acute phase proteins (APP) in the liver. However, it remains unclear how vitamin C deficiency causes these inflammation-like responses. In this study, we investigated the possibility that changes in gut microbiota are involved in the induction of APP gene expression by vitamin C deficiency. ODS rats that cannot genetically synthesize vitamin C were divided into 4 groups based on the presence or absence of vitamin C or antibiotics and were raised for 15 d. Neomycin, vancomycin, and ampicillin were used as antibiotics, and 300 mg L-ascorbic acid/kg was added to the AIN93G diet. Vitamin C deficiency affected neither the wet tissue weights nor relative abundance of bacteria in the cecal contents. Antibiotic administration increased wet weights of the cecum, cecal contents, and colon, changed the relative abundance of some bacteria in the cecal contents, and decreased serum IL-6 level. However, antibiotic administration had no effect on serum concentrations of corticosterone and α1-acid glycoprotein (AGP), vitamin C concentration in the liver, and mRNA levels of haptoglobin and AGP in the liver. Therefore, disturbance of gut microbiota did not attenuate the increase in glucocorticoid level and induction of APP gene expression due to vitamin C deficiency. This suggests that gut microbiota is not involved in the inflammation-like responses caused by vitamin C deficiency.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific CD4

SARS-CoV-2-specific CD4+ T cells are likely important in immunity against COVID-19, but our understanding of CD4+ longitudinal dynamics following infection and specific features that correlate with the maintenance of neutralizing antibodies remains limited. We characterized SARS-CoV-2-specific CD4+ T cells in a longitudinal cohort of 109 COVID-19 outpatients. The quality of the SARS-CoV-2-specific CD4+ response shifted from cells producing IFNγ to TNFα+ from five days to four months post-enrollment, with IFNγ-IL21-TNFα+ CD4+ T cells the predominant population detected at later timepoints. Greater percentages of IFNγ-IL21-TNFα+ CD4+ T cells on day 28 correlated with SARS-CoV-2 neutralizing antibodies measured seven months post-infection (ρ=0.4, P=0.01). mRNA vaccination following SARS-CoV-2 infection boosted both IFNγ and TNFα producing, spike protein-specific CD4+ T cells. These data suggest that SARS-CoV-2-specific, TNFα-producing CD4+ T cells may play an important role in antibody maintenance following COVID-19.

The mean age of dengue hemorrhagic fever (DHF) cases increased considerably in Thailand from 8.1 to 24.3 y between 1981 and 2017 (mean annual increase of 0.45 y). Alternative proposed explanations for this trend, such as changes in surveillance practices, reduced mosquito–human contact, and shifts in population demographics, have different implications for global dengue epidemiology. To evaluate the contribution of each of these hypothesized mechanisms to the observed data, we developed 20 nested epidemiological models of dengue virus infection, allowing for variation over time in population demographics, infection hazards, and reporting rates. We also quantified the effect of removing or retaining each source of variation in simulations of the age trajectory. Shifts in the age structure of susceptibility explained 58% of the observed change in age. Adding heterogeneous reporting by age and reductions in per-serotype infection hazard to models with shifts in susceptibility explained an additional 42%. Reductions in infection hazards were mostly driven by changes in the number of infectious individuals at any time (another consequence of shifting age demographics) rather than changes in the transmissibility of individual infections. We conclude that the demographic transition drives the overwhelming majority of the observed change as it changes both the age structure of susceptibility and the number of infectious individuals. With the projected Thai population age structure, our results suggest a continuing increase in age of DHF cases, shifting the burden toward individuals with more comorbidity. These insights into dengue epidemiology may be relevant to many regions of the globe currently undergoing comparable changes in population demographics.

Serosurveys are a key resource for measuring SARS-CoV-2 cumulative incidence. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce adjusted estimates of seroprevalence from raw serosurvey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a post-infection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce adjusted seroprevalence estimates from five large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identify substantial differences between reported and adjusted estimates of over two-fold in the results of some surveys, and provide a tool for practitioners to generate adjusted estimates with pre-set or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

Pegylated liposomal doxorubicin (PLD) is an anthracycline anticancer agent used in ovarian cancer and a form of doxorubicin enclosed in pegylated liposomes. There are only a few reports on intertrigo-like eruptions caused by PLD. We describe the first case of severe bullous erythema, including intertrigo-like eruptions with angioedema, induced by PLD in Japan. We present the case of a 53-year-old woman who was diagnosed with stage IIIC ovarian cancer. After receiving three cycles of PLD, the patient developed swelling of the upper lip and painful erythema with blisters and erosions on the axilla, upper back, flank and wrists. The patient was diagnosed with angioedema and severe skin lesions, including intertrigo-like eruptions induced by PLD. Although treatment with oral prednisolone and topical steroids was effective against these eruptions, the administration of PLD was discontinued because of its ineffectiveness against the primary disease. Several risk factors, such as obesity, perspiration and racial differences, may contribute toward a severe manifestation such as that seen in our patient. Moreover, our case was the first accompanied by angioedema. The mechanism of coexistence of intertrigo-like eruptions and angioedema is not clear; further studies are required to clarify the pathological mechanism of intertrigo-like eruptions.