Visualizing clinical evidence: citation networks for the incubation periods of respiratory viral infections

Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Introduction

In order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis.

Methods

Our proposed methodology trains four deep learning algorithms on an input dataset consisting of 920 patient respiratory audio files. These audio files were recorded using digital stethoscopes and comprise the Respiratory Sound Database. The four deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), and CNN ensembled with bidirectional LSTM (CNN-BLSTM).

Results

The aforementioned models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The best performing algorithm, LSTM, has an overall accuracy of 98.82% and F1-score of 0.97.

Discussion

The LSTM algorithm's extremely high predictive accuracy can be attributed to its penchant for capturing sequential patterns in time series based audio data. In summary, this algorithm is able to ingest patient audio recordings and make precise lung disease predictions in real-time.

Distinguishing viruses responsible for influenza-like illness

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

Rhinoviruses: molecular diversity and clinical characteristics

BACKGROUND

Rhinoviruses are commonly considered simple "common cold" agents. The link between their molecular epidemiology and patient clinical presentation and outcomes remains unclear in adult populations.

MATERIALS/METHODS

All nasopharyngeal or bronchoalveolar lavages were screened using multiplex PCR in three Parisian hospitals from January to September 2018. For all detected rhinoviruses, the VP2/VP4 region was subtyped by sequencing.

RESULTS

The study included 178 human rhinovirus (HRV) positive unique patients. They were primarily male (56%), with a median age of 62.2 [IQR: 46.8-71.4], frequently presenting chronic respiratory diseases (56%) and/or immunosuppression (46%). Of these, 63% were admitted for respiratory distress, including pneumonia for 25%; 95 (53%), 27 (15%), and 56 (32%) were positive for HRV-A, -B, and -C, respectively. HRV-B appeared more associated with immunosuppressive treatments (58% vs. 30% and 36% of patients for HRV-A and -C, respectively, p = 0.038), higher coinfection rates (54% vs. 34% and 23%, p = 0.03), and higher ICU admission rates (35% vs. 17% and 13%, p = 0.048). Conversely, HRV-A was more frequently associated with pneumonia (54% vs. 31% and 11% for HRV-B and -C, respectively, p = 0.01).

CONCLUSIONS

This study highlights the high proportion of chronic respiratory diseases or immunosuppression among hospitalized patients infected with a rhinovirus.

In pursuit of the right tail for the COVID-19 incubation period

Definition of the incubation period for COVID-19 is critical for implementing quarantine and thus infection control. Whereas the classical definition relies on the time from exposure to time of first symptoms, a more practical working definition is the time from exposure to time of first live virus excretion. For COVID-19, average incubation period times commonly span 5–7 days which are generally longer than for most typical other respiratory viruses. There is considerable variability reported however for the late right-hand statistical distribution. A small but yet epidemiologically important subset of patients may have the late end of the incubation period extend beyond the 14 days that is frequently assumed. Conservative assumptions of the right tail end distribution favor safety, but pragmatic working modifications may be required to accommodate high rates of infection and/or healthcare worker exposures. Despite the advent of effective vaccines, further attention and study in these regards are warranted. It is predictable that vaccine application will be associated with continued confusion over protection and its longevity. Measures for the application of infectivity will continue to be extremely relevant.

Cross-infection of adenovirus among medical staff: A warning from the intensive care unit in a tertiary care teaching hospital in China

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Human adenovirus-55 in a single patient had strong transmission potential in ICU.

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This infectious event involved more than 20 medical staff members in adult ICU.

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Contact with patient, lack of hand hygiene or gloving adherence, were risk factors.

Rationale

In 2019, a small HAdV55-associated outbreak of adenovirus infection occurred among the intensive care unit (ICU) staff in Xiangya Hospital of Central South University in Hunan Province, China, during the treatment of a patient.

Objective

To investigate the characteristics of a nosocomial adenovirus outbreak in an ICU.

Methods

We evaluated all the patients treated and the medical staff working in the ICU from August 1 to September 4, 2019. We further performed an epidemiological and molecular analysis for this outbreak from patient to healthcare workers and between healthcare workers. After the outbreak, we adopted exposure prevention and droplet prevention measures based on standard precautions.

Measurements and main results

Between August 1 and August 27, 2019, 27 cases of human adenovirus cross-infection were reported in our institution. Among the cases, eleven were doctors (41%), eleven were nurses (41%), three were respiratory therapists (11%), and two were caregivers (7%). The attack rate was 28.4%, and the fatality rate was 0. The results showed that contact with the index case, lack of hand hygiene or gloving adherence were risk factors for infection after adenovirus exposure. After taking specific precautions, no new cases of infection have appeared since August 27.

Conclusions

Our results show that HAdV55 in a single patient had strong transmission potential in an intensive care unit with adequate facilities and standardized operation. We provide convincing evidence indicating that attention could be highlighted on the role of standard and specific precautions for controlling the spread of adenovirus in ICUs.

Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

Times to key events in Zika virus infection and implications for blood donation: a systematic review

Objective

To estimate the timing of key events in the natural history of Zika virus infection.

Methods

In February 2016, we searched PubMed, Scopus and the Web of Science for publications containing the term Zika. By pooling data, we estimated the incubation period, the time to seroconversion and the duration of viral shedding. We estimated the risk of Zika virus contaminated blood donations.

Findings

We identified 20 articles on 25 patients with Zika virus infection. The median incubation period for the infection was estimated to be 5.9 days (95% credible interval, CrI: 4.4–7.6), with 95% of people who developed symptoms doing so within 11.2 days (95% CrI: 7.6–18.0) after infection. On average, seroconversion occurred 9.1 days (95% CrI: 7.0–11.6) after infection. The virus was detectable in blood for 9.9 days (95% CrI: 6.9–21.4) on average. Without screening, the estimated risk that a blood donation would come from an infected individual increased by approximately 1 in 10 000 for every 1 per 100 000 person–days increase in the incidence of Zika virus infection. Symptom-based screening may reduce this rate by 7% (relative risk, RR: 0.93; 95% CrI: 0.89–0.99) and antibody screening, by 29% (RR: 0.71; 95% CrI: 0.28–0.88).

Conclusion

Neither symptom- nor antibody-based screening for Zika virus infection substantially reduced the risk that blood donations would be contaminated by the virus. Polymerase chain reaction testing should be considered for identifying blood safe for use in pregnant women in high-incidence areas.

Revisiting the role of environmental and climate factors on the epidemiology of Kawasaki disease

Can environmental factors, such as air-transported preformed toxins, be of key relevance to the health outcomes of poorly understood human ailments (e.g., rheumatic diseases such as vasculitides, some inflammatory diseases, or even severe childhood acquired heart diseases)? Can the physical, chemical, or biological features of air masses be linked to the emergence of diseases such as Kawasaki disease (KD), Henoch-Schönlein purpura, Takayasu's aortitis, and ANCA-associated vasculitis? These diseases surprisingly share some common epidemiological features. For example, they tend to appear as clusters of cases grouped geographically and temporarily progress in nonrandom sequences that repeat every year in a similar way. They also show concurrent trend changes within regions in countries and among different world regions. In this paper, we revisit transdisciplinary research on the role of environmental and climate factors in the epidemiology of KD as a paradigmatic example of this group of diseases. Early-warning systems based on environmental alerts, if successful, could be implemented as a way to better inform patients who are predisposed to, or at risk for, developing KD. Further research on the etiology of KD could facilitate the development of vaccines and specific medical therapies.

Association of host, agent and environment characteristics and the duration of incubation and symptomatic periods of norovirus gastroenteritis

We analysed the reported duration of incubation and symptomatic periods of norovirus for a dataset of 1022 outbreaks, 64 of which reported data on the average incubation period and 87 on the average symptomatic period. We found the mean and median incubation periods for norovirus to be 32·8 [95% confidence interval (CI) 30·9-34·6] hours and 33·5 (95% CI 32·0-34·0) hours, respectively. For the symptomatic period we found the mean and median to be 44·2 (95% CI 38·9-50·7) hours and 43·0 (95% CI 36·0-48·0) hours, respectively. We further investigated how these average periods were associated with several reported host, agent and environmental characteristics. We did not find any strong, biologically meaningful associations between the duration of incubation or symptomatic periods and the reported host, pathogen and environmental characteristics. Overall, we found that the distributions of incubation and symptomatic periods for norovirus infections are fairly constant and showed little differences with regard to the host, pathogen and environmental characteristics we analysed.

Tropospheric winds from northeastern China carry the etiologic agent of Kawasaki disease from its source to Japan

Evidence indicates that the densely cultivated region of northeastern China acts as a source for the wind-borne agent of Kawasaki disease (KD). KD is an acute, coronary artery vasculitis of young children, and still a medical mystery after more than 40 y. We used residence times from simulations with the flexible particle dispersion model to pinpoint the source region for KD. Simulations were generated from locations spanning Japan from days with either high or low KD incidence. The postepidemic interval (1987-2010) and the extreme epidemics (1979, 1982, and 1986) pointed to the same source region. Results suggest a very short incubation period (<24 h) from exposure, thus making an infectious agent unlikely. Sampling campaigns over Japan during the KD season detected major differences in the microbiota of the tropospheric aerosols compared with ground aerosols, with the unexpected finding of the Candida species as the dominant fungus from aloft samples (54% of all fungal strains). These results, consistent with the Candida animal model for KD, provide support for the concept and feasibility of a windborne pathogen. A fungal toxin could be pursued as a possible etiologic agent of KD, consistent with an agricultural source, a short incubation time and synchronized outbreaks. Our study suggests that the causative agent of KD is a preformed toxin or environmental agent rather than an organism requiring replication. We propose a new paradigm whereby an idiosyncratic immune response, influenced by host genetics triggered by an environmental exposure carried on winds, results in the clinical syndrome known as acute KD.

Incubation periods of mosquito-borne viral infections: a systematic review

Mosquito-borne viruses are a major public health threat, but their incubation periods are typically uncited, non-specific, and not based on data. We systematically review the published literature on six mosquito-borne viruses selected for their public health importance: chikungunya, dengue, Japanese encephalitis, Rift Valley fever, West Nile, and yellow fever viruses. For each, we identify the literature's consensus on the incubation period, evaluate the evidence for this consensus, and provide detailed estimates of the incubation period and distribution based on published experimental and observational data. We abstract original data as doubly interval-censored observations. Assuming a log-normal distribution, we estimate the median incubation period, dispersion, 25th and 75th percentiles by maximum likelihood. We include bootstrapped 95% confidence intervals for each estimate. For West Nile and yellow fever viruses, we also estimate the 5th and 95th percentiles of their incubation periods.

Incubation periods of viral gastroenteritis: a systematic review

BACKGROUND

Accurate knowledge of incubation period is important to investigate and to control infectious diseases and their transmission, however statements of incubation period in the literature are often uncited, inconsistent, and/or not evidence based.

METHODS

In a systematic review of the literature on five enteric viruses of public health importance, we found 256 articles with incubation period estimates, including 33 with data for pooled analysis.

RESULTS

We fit a log-normal distribution to pooled data and found the median incubation period to be 4.5 days (95% CI 3.9-5.2 days) for astrovirus, 1.2 days (95% CI 1.1-1.2 days) for norovirus genogroups I and II, 1.7 days (95% CI 1.5-1.8 days) for sapovirus, and 2.0 days (95% CI 1.4-2.4 days) for rotavirus.

CONCLUSIONS

Our estimates combine published data and provide sufficient quantitative detail to allow for these estimates to be used in a wide range of clinical and modeling applications. This can translate into improved prevention and control efforts in settings with transmission or the risk of transmission.