Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region

Background

A common challenge to the study of several infectious diseases consists in combining limited cross-sectional survey data, collected with a more sensitive detection method, with a more extensive (but biased) syndromic sentinel surveillance data, collected with a less sensitive method. Our article describes a novel modeling framework that overcomes this challenge, resulting in enhanced understanding of malaria in the Western Brazilian Amazon.

Methodology/Principal Findings

A cohort of 486 individuals was monitored using four cross-sectional surveys, where all participants were sampled regardless of symptoms (aggressive-active case detection), resulting in 1,383 microscopy and 1,400 polymerase chain reaction tests. Data on the same individuals were also obtained from the local surveillance facility (i.e., passive and active case detection), totaling 1,694 microscopy tests. Our model accommodates these multiple pathogen and case detection methods. This model is shown to outperform logistic regression in terms of interpretability of its parameters, ability to recover the true parameter values, and predictive performance. We reveal that the main infection determinant was the extent of forest, particularly during the rainy season and in close proximity to water bodies, and participation on forest activities. We find that time residing in Acrelandia (as a proxy for past malaria exposure) decreases infection risk but surprisingly increases the likelihood of reporting symptoms once infected, possibly because non-naïve settlers are only susceptible to more virulent Plasmodium strains. We suggest that the search for asymptomatic carriers should focus on those at greater risk of being infected but lower risk of reporting symptoms once infected.

Conclusions/Significance

The modeling framework presented here combines cross-sectional survey data and syndromic sentinel surveillance data to shed light on several aspects of malaria that are critical for public health policy. This framework can be adapted to enhance inference on infectious diseases whenever asymptomatic carriers are important and multiple datasets are available.     

An individual level infectious disease model in the presence of uncertainty from multiple,imperfect diagnostic tests

Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false-positives and false-negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio-temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. Although all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested.     

Modeling and real-time prediction of classical swine fever epidemics

We propose a new method to analyze outbreak data of an infectious disease such as classical swine fever. The underlying model is a two-type branching process. It is used to deduce information concerning the epidemic from detected cases. In particular, the method leads to prediction of the future course of the epidemic and hence can be used as a basis for control policy decisions. We test the model with data from the large 1997-1998 classical swine fever epidemic in The Netherlands. It turns out that our results are in good agreement with the data.     

Hemorrhagic fever with renal syndrome associated with acute pancreatitis

Hemorrhagic fever with renal syndrome (HFRS) is a systemic infectious disease caused by Hantaviruses and characterized by fevers,bleeding tendencies,gastrointestinal symptoms and renal failure.It encom...     

A two-component model for counts of infectious diseases

We propose a stochastic model for the analysis of time series of disease counts as collected in typical surveillance systems on notifiable infectious diseases. The model is based on a Poisson or negative binomial observation model with two components: a parameter-driven component relates the disease incidence to latent parameters describing endemic seasonal patterns, which are typical for infectious disease surveillance data. An observation-driven or epidemic component is modeled with an autoregression on the number of cases at the previous time points. The autoregressive parameter is allowed to change over time according to a Bayesian changepoint model with unknown number of changepoints. Parameter estimates are obtained through the Bayesian model averaging using Markov chain Monte Carlo techniques. We illustrate our approach through analysis of simulated data and real notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin. Software to fit the proposed model can be obtained from http://www.statistik.lmu.de/ approximately mhofmann/twins.     

A Hybrid EM and Monte Carlo EM Algorithm and Its Application to Analysis of Transmission of Infectious Diseases

Summary In epidemics of infectious diseases such as influenza, an individual may have one of four possible final states: prior immune, escaped from infection, infected with symptoms, and infected asymptomatically. The exact state is often not observed. In addition, the unobserved transmission times of asymptomatic infections further complicate analysis. Under the assumption of missing at random, data‐augmentation techniques can be used to integrate out such uncertainties. We adapt an importance‐sampling‐based Monte Carlo Expectation‐Maximization (MCEM) algorithm to the setting of an infectious disease transmitted in close contact groups. Assuming the independence between close contact groups, we propose a hybrid EM‐MCEM algorithm that applies the MCEM or the traditional EM algorithms to each close contact group depending on the dimension of missing data in that group, and discuss the variance estimation for this practice. In addition, we propose a bootstrap approach to assess the total Monte Carlo error and factor that error into the variance estimation. The proposed methods are evaluated using simulation studies. We use the hybrid EM‐MCEM algorithm to analyze two influenza epidemics in the late 1970s to assess the effects of age and preseason antibody levels on the transmissibility and pathogenicity of the viruses.     

A concentration-dependent analysis method for high density protein microarrays

Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.     

Distribution of Paenibacillus larvae spores inside honey bee colonies and its relevance for diagnosis

One of the most important factors affecting the development of honey bee colonies is infectious diseases such as American foulbrood (AFB) caused by the spore forming Gram-positive bacterium Paenibacillus larvae. Colony inspections for AFB clinical symptoms are time consuming. Moreover, diseased cells in the early stages of the infection may easily be overlooked. In this study, we investigated whether it is possible to determine the sanitary status of a colony based on analyses of different materials collected from the hive. We analysed 237 bee samples and 67 honey samples originating from 71 colonies situated in 13 apiaries with clinical AFB occurrences. We tested whether a difference in spore load among bees inside the whole hive exists and which sample material related to its location inside the hive was the most appropriate for an early AFB diagnosis based on the culture method. Results indicated that diagnostics based on analysis of honey samples and bees collected at the hive entrance are of limited value as only 86% and 83%, respectively, of samples from AFB-symptomatic colonies were positive. Analysis of bee samples collected from the brood nest, honey chamber, and edge frame allowed the detection of all colonies showing AFB clinical symptoms. Microbiological analysis showed that more than one quarter of samples collected from colonies without AFB clinical symptoms were positive for P. larvae. Based on these results, we recommend investigating colonies by testing bee samples from the brood nest, edge frame or honey chamber for P. larvae spores.     

Premenstrual syndrome: an evolutionary perspective on its causes and treatment

Premenstrual syndrome is a collection of heterogeneous symptoms that are attributed to hormonal fluctuations and that vary among individuals for unknown reasons. We propose that much of what is labeled "premenstrual syndrome" is part of a broader set of infectious illnesses that are exacerbated by cyclic changes in immunosuppression, which are induced by cyclic changes in estrogen and progesterone. This cyclic defense paradigm accords with the literature on cyclic exacerbations of persistent infectious diseases and chronic diseases of uncertain cause. Similar exacerbations attributable to hormonal contraception implicate hormonal alterations as a cause of these changes. The precise timing of these cyclic exacerbations depends on the mechanisms of pathogenesis and immunological control of particular infectious agents. Insight into these mechanisms can be obtained by a comparison of timing of menstrual exacerbations with the timing of exacerbations associated with pregnancy.     

Epidemiology, transmission dynamics and control of SARS: the 2002-2003 epidemic

This paper reviews current understanding of the epidemiology, transmission dynamics and control of the aetiological agent of severe acute respiratory syndrome (SARS). We present analyses of data on key parameters and distributions and discuss the processes of data capture, analysis and public health policy formulation during the SARS epidemic are discussed. The low transmissibility of the virus, combined with the onset of peak infectiousness following the onset of clinical symptoms of disease, transpired to make simple public health measures, such as isolating patients and quarantining their contacts, very effective in the control of the SARS epidemic. We conclude that we were lucky this time round, but may not be so with the next epidemic outbreak of a novel aetiological agent. We present analyses that help to further understanding of what intervention measures are likely to work best with infectious agents of defined biological and epidemiological properties. These lessons learnt from the SARS experience are presented in an epidemiological and public health context.     

Identification of candidate T-cell epitopes and molecular mimics in chronic Lyme disease

Elucidating the cellular immune response to infectious agents is a prerequisite for understanding disease pathogenesis and designing effective vaccines. In the identification of microbial T-cell epitopes, the availability of purified or recombinant bacterial proteins has been a chief limiting factor. In chronic infectious diseases such as Lyme disease, immune-mediated damage may add to the effects of direct infection by means of molecular mimicry to tissue autoantigens. Here, we describe a new method to effectively identify both microbial epitopes and candidate autoantigens. The approach combines data acquisition by positional scanning peptide combinatorial libraries and biometric data analysis by generation of scoring matrices. In a patient with chronic neuroborreliosis, we show that this strategy leads to the identification of potentially relevant T-cell targets derived from both Borrelia burgdorferi and the host. We also found that the antigen specificity of a single T-cell clone can be degenerate and yet the clone can preferentially recognize different peptides derived from the same organism, thus demonstrating that flexibility in T-cell recognition does not preclude specificity. This approach has potential applications in the identification of ligands in infectious diseases, tumors and autoimmune diseases.     

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.     

Modeling the onset of symptoms of COVID-19: Effects of SARS-CoV-2 variant

Identifying order of symptom onset of infectious diseases might aid in differentiating symptomatic infections earlier in a population thereby enabling non-pharmaceutical interventions and reducing disease spread. Previously, we developed a mathematical model predicting the order of symptoms based on data from the initial outbreak of SARS-CoV-2 in China using symptom occurrence at diagnosis and found that the order of COVID-19 symptoms differed from that of other infectious diseases including influenza. Whether this order of COVID-19 symptoms holds in the USA under changing conditions is unclear. Here, we use modeling to predict the order of symptoms using data from both the initial outbreaks in China and in the USA. Whereas patients in China were more likely to have fever before cough and then nausea/vomiting before diarrhea, patients in the USA were more likely to have cough before fever and then diarrhea before nausea/vomiting. Given that the D614G SARS-CoV-2 variant that rapidly spread from Europe to predominate in the USA during the first wave of the outbreak was not present in the initial China outbreak, we hypothesized that this mutation might affect symptom order. Supporting this notion, we found that as SARS-CoV-2 in Japan shifted from the original Wuhan reference strain to the D614G variant, symptom order shifted to the USA pattern. Google Trends analyses supported these findings, while weather, age, and comorbidities did not affect our model’s predictions of symptom order. These findings indicate that symptom order can change with mutation in viral disease and raise the possibility that D614G variant is more transmissible because infected people are more likely to cough in public before being incapacitated with fever.     

Acute necrotizing encephalopathy: A comparison between influenza and non-influenza cases

The aim of this study was to clarify the difference between influenza and non-influenza cases in clinical symptoms, laboratory and neuroimaging findings, and outcome in children with ANE. We retrospectively studied 22 children with ANE. Eleven of them had virological proof of influenza infection and the other 11 were judged as non-influenza infection. There was no significant difference between influenza and non-influenza cases in sex, antipyretics use and neurological symptoms. Although laboratory data were not different between the two groups, brainstem lesions were relatively more frequent in influenza cases than in non-influenza cases. Outcome was not different between the two groups. The results of our study suggest that the pathogenesis of acute necrotizing encephalopathy will not be dependent on infectious agents.     

复方嗜酸乳杆菌片对感染性腹泻患者胃肠道症状的影响

目的应用复方嗜酸乳杆菌片治疗感染性腹泻,观察微生态制剂对患者胃肠道症状的影响。方法选择100例感染性腹泻患者,均来自青岛大学附属中心医院,采用随机数字表法随机分为A、B组。其中A组患者(80例)单用复方嗜酸乳杆菌片(1.0g/次,3次/d,口服)治疗;B组患者(20例)单用利福昔明片(0.2g/次,4次/d,口服)治疗。两组患者均采用配对设计方法,治疗前进行胃肠道症状分级评分,包括单个症状(腹痛、腹胀、腹泻频率、大便性状)的评分和上述症状的总评分。治疗3d后门诊随访或电话随访,再次进行胃肠道症状分级评分,比较治疗前后患者的胃肠道症状评分改善情况并判断疗效以及治疗中的不良反应。结果两组患者的总有效率分别为78.75%和85.00%,差异无统计学意义(P0.05)。两组患者治疗过程中均未见不良反应。复方嗜酸乳杆菌片治疗后患者腹胀、腹泻频率、大便性状的症状评分和症状总评分较治疗前显著降低,差异有统计学意义(P0.01)。腹痛症状评分治疗前后差异无统计学意义(P0.05)。结论复方嗜酸乳杆菌片治疗感染性腹泻能达到满意的疗效,可改善患者腹胀、腹泻频率、大便性状等胃肠道症状。     

Linking Time-Varying Symptomatology and Intensity of Infectiousness to Patterns of Norovirus Transmission

Background

Norovirus (NoV) transmission may be impacted by changes in symptom intensity. Sudden onset of vomiting, which may cause an initial period of hyper-infectiousness, often marks the beginning of symptoms. This is often followed by: a 1–3 day period of milder symptoms, environmental contamination following vomiting, and post-symptomatic shedding that may result in transmission at progressively lower rates. Existing models have not included time-varying infectiousness, though representing these features could add utility to models of NoV transmission.

Methods

We address this by comparing the fit of three models (Models 1–3) of NoV infection to household transmission data from a 2009 point-source outbreak of GII.12 norovirus in North Carolina. Model 1 is an SEIR compartmental model, modified to allow Gamma-distributed sojourn times in the latent and infectious classes, where symptomatic cases are uniformly infectious over time. Model 2 assumes infectiousness decays exponentially as a function of time since onset, while Model 3 is discontinuous, with a spike concentrating 50% of transmissibility at onset. We use Bayesian data augmentation techniques to estimate transmission parameters for each model, and compare their goodness of fit using qualitative and quantitative model comparison. We also assess the robustness of our findings to asymptomatic infections.

Results

We find that Model 3 (initial spike in shedding) best explains the household transmission data, using both quantitative and qualitative model comparisons. We also show that these results are robust to the presence of asymptomatic infections.

Conclusions

Explicitly representing explosive NoV infectiousness at onset should be considered when developing models and interventions to interrupt and prevent outbreaks of norovirus in the community. The methods presented here are generally applicable to the transmission of pathogens that exhibit large variation in transmissibility over an infection.     

A discrete-time model for the statistical analysis of infectious disease incidence data.

A discrete-time model is devised for the per-time-unit distribution of infectious disease cases in a sample of households. Using the time at which an individual is identified (e.g., when illness symptoms appear) as a marker for being infected, the probabilities of becoming infected from the community or from a single infectious household member are estimated for various risk factor levels. Maximum likelihood procedures for estimating the model parameters are given. An individual may be classified with regard to level of susceptibility and level of infectiousness. The model is fitted to a combination of symptom and viral culture data from a rhinovirus epidemic in Tecumseh, Michigan. In general, it is observed that decreasing risk of infection is associated with increasing age.     

An Extended,Problem-Based Learning Laboratory Exercise on the Diagnosis of Infectious Diseases Suitable for Large Level 1 Undergraduate Biology Classes

The development and evaluation of a two-week laboratory class, based on the diagnosis of human infectious diseases, is described. It can be easily scaled up or down, to suit class sizes from 50 to 600 and completed in a shorter time scale, and to different audiences as desired. Students employ a range of techniques to solve a real-life and relevant problem, and are introduced to the range and type of infectious agents, their routes of transmission and risk factors, clinical symptoms and diagnoses, and their treatment and prevention. No infectious material is used, and the practical is very inexpensive and easy to prepare. Six ‘patients’ are diagnosed, using their symptoms, patient histories, temperature records, serology, blood and faecal slide examination, and bacteriological isolation from blood, faeces and cerebrospinal fluid.     

Glycoprotein gp50-negative pseudorabies virus: a novel approach toward a nonspreading live herpesvirus vaccine.

Essential herpesvirus glycoproteins are involved in membrane fusion processes during infection, e.g., viral penetration and direct cell-to-cell transmission. We previously showed that the gD-homologous glycoprotein gp50 of pseudorabies virus (PrV) is essential for virus entry into target cells but proved to be dispensable for direct viral cell-to-cell spread in cell culture (I. Rauh and T. C. Mettenleiter, J. Virol. 65:5348-5456, 1991). For gp50-negative (gp50-) viruses, after phenotypic complementation necessary for primary infection, the only means of viral spread is by way of direct cell-to-cell transmission. In contrast, virus mutants lacking the essential gB-homologous glycoprotein gII after phenotypic complementation are only able to infect primary target cells and are blocked in further viral spread. To analyze how these in vitro phenotypes translate into virus replication in the animal, mice were infected intranasally with gp50- or gII- PrV mutants after prior phenotypic complementation by propagation on cell lines providing the essential glycoprotein in trans. Our results show that whereas the gII- mutants did not cause disease or any symptoms, gp50- mutants derived from two different PrV strains were fully virulent, with animals exhibiting severe symptoms ultimately leading to death. However, free infectious virus could not be recovered from either gp50- or gII- PrV-infected animals. We conclude that direct cell-to-cell transmission as the only means of viral spread of the gp50- mutants is sufficient for a full virulent phenotype in mice. After infection of pigs with phenotypically complemented gp50- PrV, only mild symptoms were observed, whereas the gII- mutant was totally avirulent. In both cases, shedding of infectious virus did not occur, in contrast to results with animals infected by gX- PrV that showed severe signs of disease and extensive virus shedding. After challenge infection with the highly virulent NIA-3 strain, the previously gII- PrV-infected animals exhibited severe symptoms, whereas the gp50- PrV-infected pigs showed a significant level of protection. In conclusion, vaccination with a PrV mutant lacking glycoprotein gp50, which is unable to spread between animals because of a lack of formation of free infectious virions, can confer on pigs protection against challenge infection. These results provide the basis for the development of new, nonspreading live herpesvirus vaccines based on gp50- PrV mutants.