ModInterv: An automated online software for modeling epidemics

The COVID-19 pandemic has proven the importance of mathematical tools to understand the evolution of epidemic outbreaks and provide reliable information to the general public and health authorities. In this perspective, we have developed ModInterv, an online software that applies growth models to monitor the evolution of the COVID-19 epidemic in locations chosen by the user among countries worldwide or states and cities in the USA or Brazil. This paper describes the software capabilities and its use both in recent research works and by technical committees assisting government authorities. Possible applications to other epidemics are also briefly discussed.

First wave of the COVID-19 pandemic in Madrid: handling the unexpected in a tertiary hospital

INTRODUCTION

The COVID-19 pandemic was declared on March 11^th, 2020. By the end of January, the first imported cases were detected in Spain and, by March, the number of cases was growing exponentially, causing the implementation of a national lockdown. Madrid has been one of the most affected regions in terms of both cases and deaths. The aim of this study is to describe the epidemic curve and the epidemiological features and outcomes of COVID-19 patients hospitalized in La Paz University Hospital, a tertiary hospital located in Madrid.

METHODS

We included confirmed and probable COVID-19 cases admitted to our centre from February 26^th to June 1^st, 2020. We studied trends in hospitalization and ICU admissions using joinpoint regression analysis.

RESULTS

A sample of 2970 patients was obtained. Median age was 70 years old (IQR 55-82) and 54.8% of them were male. ICU admission rate was 8.7% with a mortality rate of 45.7%. Global CFR was 21.8%. Median time from symptom onset to death was 14 days (IQR 9-22).

CONCLUSIONS

We detected an admissions peak on March 21st followed by a descending trend, matching national and regional data. Age and sex distribution were comparable to further series nationally and in western countries.

[The wave train of COVID-19 infections]

The present work studies the epidemic curve of COVID-19 in Italy between September 2020 and mid-June 2021 in terms of poussées, that is successive waves. There is obviously only one pandemic, although the virus has spread in the form of several variants, but the daily incidence trend can also be read in terms of overlapping of events that are different from each other or, in any case, induced by various phenomena. It can be hypothesized that in this way a succession of various waves was generated, which are modelled here using appropriate adaptation curves used in the study of epidemic data. Each curve corresponds approximately to the situation that would have occurred if no element had intervened to prevent the decrease of infections after the relative peak, while their overlap is considered to describe the subsequent increases. This interpolation has no predictive purpose, being purely descriptive over the time window under consideration. The discrepancies between the superposition of the modelling curves and the real epidemic curve are therefore also highlighted, especially in the transition periods between the various poussées. Finally, the analysis carried out allows to match the trend of the epidemic in the period considered with, on one hand, the series of events and, on the other, with the containment measures adopted which may have determined the succession of increases and decreases in the incidence of infections.

Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves

Objectives

The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data.

Methods

We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data.

Results

We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low.

Conclusion

Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves

OBJECTIVES

The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data.

METHODS

We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data.

RESULTS

We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low.

CONCLUSION

Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

Estimating the quarantine failure rate for COVID-19

Quarantine is a crucial control measure in reducing imported COVID-19 cases and community transmissions. However, some quarantined COVID-19 patients may show symptoms after finishing quarantine due to a long median incubation period, potentially causing community transmissions. To assess the recommended 14-day quarantine policy, we develop a formula to estimate the quarantine failure rate from the incubation period distribution and the epidemic curve. We found that the quarantine failure rate increases with the exponential growth rate of the epidemic curve. We apply our formula to United States, Canada, and Hubei Province, China. Before the lockdown of Wuhan City, the quarantine failure rate in Hubei Province is about 4.1%. If the epidemic curve flattens or slowly decreases, the failure rate is less than 2.8%. The failure rate in US may be as high as 8.3%–11.5% due to a shorter 10-day quarantine period, while the failure rate in Canada may be between 2.5% and 3.9%. A 21-day quarantine period may reduce the failure rate to 0.3%–0.5%.

A reactive vaccination campaign with single dose oral cholera vaccine (OCV) during a cholera outbreak in Cameroon

BACKGROUND

Cameroon chose Oral Cholera Vaccine (OCV) mass vaccination campaign in addition to other interventions to respond to outbreaks since 2015. There is still a persistent controversy on the effectiveness of reactive OCV mass vaccination campaign.

OBJECTIVE

This article aimed to share evidence-based observations on the effect of a reactive single-dose OCV mass vaccination campaign on cholera cases in Cameroon.

METHODS

Health area centered risk analysis was used to identify nine high risk health areas among four health districts in the |North Region as hotspots. About 537,274 people at risk of cholera transmission one year of age and above including pregnant women were eligible to receive OCV. A total of 537,279 doses of OCV was deployed for vaccination from August 1-5, 2019 through door-to-door strategy for urban health districts, and fixed/ temporary fixed posts strategies for rural health districts.

RESULTS

The overall vaccination coverage was 99.9%. Vaccine wastage rate was less than 0.5% (0.0011%). Independent monitoring showed vaccination coverage at 97.2%. The 2019 epidemic curve went down after OCV intervention on the contrary to that in the year 2018 at the same period. After OCV intervention, cholera cases dropped from about 10.5 to 9.3 cases per week at the regional level while at the district level, they dropped from 5.3 to 2.1, 2.2 to 1.7, 0.6 to 0 and 1.7 to 1.5 cases per week respectively for Garoua, Garoua II, Tchollire and Pitoa. Though not statistically significant (p = 1.4, α = 0.05), cases per 1000 population seemed to remain unchanged among OCV zones (0.32/1000) and non-OCV zones (0.31/1000) in 2018 while they increased from 0.37 (OCV zones) to 0.53 (non-0CV zones) cases per 1000 population in 2019.

CONCLUSION

There might have been a general trend in the reduction of the number of new cases after a reactive single-dose OCV campaign.

COVID-19: Nothing is Normal in this Pandemic

This manuscript brings attention to inaccurate epidemiological concepts that emerged during the COVID-19 pandemic. In social media and scientific journals, some wrong references were given to a “normal epidemic curve” and also to a “log-normal curve/distribution”. For many years, textbooks and courses of reputable institutions and scientific journals have disseminated misleading concepts. For example, calling histogram to plots of epidemic curves or using epidemic data to introduce the concept of a Gaussian distribution, ignoring its temporal indexing. Although an epidemic curve may look like a Gaussian curve and be eventually modelled by a Gauss function, it is not a normal distribution or a log-normal, as some authors claim. A pandemic produces highly-complex data and to tackle it effectively statistical and mathematical modelling need to go beyond the “one-size-fits-all solution”. Classical textbooks need to be updated since pandemics happen and epidemiology needs to provide reliable information to policy recommendations and actions.

Second wave of Covid-19 is determined by immune mechanism

A second wave of new severe acute respiratory syndrome coronavirus 2 (Covid-19) cases is widely feared. In fact resurgence of cases has been clearly observed in several countries that had seen flattening of the epidemic curve. In general, relaxation of community control measures is almost always blamed for the resurgence of cases. In this letter, the author describes an immunological explanation for the double-peaked epidemic curve of new viral diseases including Covid-19. According to this hypothesis, a second wave of cases is due to the effective innate immunity in some of the population. These individuals may later develop clinical disease upon repeated exposure. This theory claims that a double-peaked pattern of new cases in a new viral epidemic is intrinsically determined by the pattern of pathogen interaction with the host. According to this hypothesis, relaxation of the community control measures is not responsible; at least in part, for resurgence of cases.

Alternative graphical displays for the monitoring of epidemic outbreaks, with application to COVID-19 mortality

BACKGROUND

Classic epidemic curves - counts of daily events or cumulative events over time -emphasise temporal changes in the growth or size of epidemic outbreaks. Like any graph, these curves have limitations: they are impractical for comparisons of large and small outbreaks or of asynchronous outbreaks, and they do not display the relative growth rate of the epidemic. Our aim was to propose two additional graphical displays for the monitoring of epidemic outbreaks that overcome these limitations.

METHODS

The first graph shows the growth of the epidemic as a function of its size; specifically, the logarithm of new cases on a given day, N(t), is plotted against the logarithm of cumulative cases C(t). Logarithm transformations facilitate comparisons of outbreaks of different sizes, and the lack of a time scale overcomes the need to establish a starting time for each outbreak. Notably, on this graph, exponential growth corresponds to a straight line with a slope equal to one. The second graph represents the logarithm of the relative rate of growth of the epidemic over time; specifically, log₁₀(N(t)/C(t-1)) is plotted against time (t) since the 25th event. We applied these methods to daily death counts attributed to COVID-19 in selected countries, reported up to June 5, 2020.

RESULTS

In most countries, the log(N) over log(C) plots showed initially a near-linear increase in COVID-19 deaths, followed by a sharp downturn. They enabled comparisons of small and large outbreaks (e.g., Switzerland vs UK), and identified outbreaks that were still growing at near-exponential rates (e.g., Brazil or India). The plots of log₁₀(N(t)/C(t-1)) over time showed a near-linear decrease (on a log scale) of the relative growth rate of most COVID-19 epidemics, and identified countries in which this decrease failed to set in in the early weeks (e.g., USA) or abated late in the outbreak (e.g., Portugal or Russia).

CONCLUSIONS

The plot of log(N) over log(C) displays simultaneously the growth and size of an epidemic, and allows easy identification of exponential growth. The plot of the logarithm of the relative growth rate over time highlights an essential parameter of epidemic outbreaks.

An analysis of COVID-19 spread based on fractal interpolation and fractal dimension

The present paper proposes a reconstruction of the epidemic curves from the fractal interpolation point of view. Looking at the epidemic curves as fractal structures might be an efficient way to retrieve missing pieces of information due to insufficient testing and predict the evolution of the disease. A fractal approach of the epidemic curve can contribute to the assessment and modeling of other epidemics. On the other hand, we have considered the spread of the epidemic in countries like Romania, Italy, Spain, and Germany and analyzed the spread of the disease in those countries based on their fractal dimension.

Epidemic characteristics of the COVID-19 outbreak in Tianjin, a well-developed city in China

BACKGROUND

Coronavirus disease 2019 (COVID-19) is already a pandemic. Few studies investigated the epidemic characteristics of the COVID-19 outbreak in the well-developed cities.

METHODS

Epidemiological data of 136 confirmed COVID-19 cases were collected from the dataset of COVID-19 in Tianjin. All confirmed cases were categorized according to their potential infection sources. Daily numbers of confirmed cases of each category were plotted by date of onset, and the epidemic form of each category was inferred.

RESULTS

Among the 136 confirmed COVID-19 cases, 48 cases were categorized as imported cases and their close contacts, which were the majority of early cases. A total of 43 cases were found an epidemiological link to the Baodi department store, and they were inferred to be a common-source outbreak. Additionally, 35 cases were considered as familial clusters of COVID-19 cases, and 10 cases were sporadic. The 45 cases were inferred to be a propagated epidemic.

CONCLUSIONS

Local transmission of COVID-19 mainly occurred within families and a poorly ventilated public place in Tianjin. Besides the imported cases, the pattern of local transmission of COVID-19 was a mixture of the propagated epidemic and the common-source outbreak in Tianjin.

Canada's role in strengthening global health security during the COVID-19 pandemic

The world is confronted by the current pandemic of Corona Virus Disease (COVID-19), which is a wake-up call for all nations irrespective of their development status or geographical location. Since the start of the century we have seen five big infectious outbreaks which proved that epidemics are no more regarded as historic and geographically confined threats. The Canadian government underlined that these infectious disease outbreaks are threats to global health security and disrupt societal wellbeing and development. In this context, the Public Health Agency of Canada is proactive and has shown its preparedness for outbreaks of emerging and epidemic-prone diseases, and in dealing with these pathogens. Even before the declaration of pandemic, Canada has proved its global health leadership by ensuring collective action and multisectoral coordination which still remains a serious challenge especially for low and middle- income countries with existing poor health systems. In this article we discuss how Canada is addressing the global challenges posed by the COVID-19 pandemic through its leadership and practice of global health diplomacy.

Estimating epidemic exponential growth rate and basic reproduction number

The initial exponential growth rate of an epidemic is an important measure of the severeness of the epidemic, and is also closely related to the basic reproduction number. Estimating the growth rate from the epidemic curve can be a challenge, because of its decays with time. For fast epidemics, the estimation is subject to over-fitting due to the limited number of data points available, which also limits our choice of models for the epidemic curve. We discuss the estimation of the growth rate using maximum likelihood method and simple models.

[Evaluation of real-time surveillance of influenza incidence in Kawasaki City by comparison using the National Epidemiological Surveillance of Infectious Diseases]

Objectives　In Japan, nationwide data of the incidence of infectious diseases have been collected via the National Epidemiological Surveillance of Infectious Diseases (NESID) since 1981. In addition, since March 2014, Kawasaki City has operated its own real-time surveillance (RTS) system to collect data of the incidence of influenza from medical institutions across the city. This study aimed to describe the characteristics of the RTS system and compare the two surveillance systems to improve measures against infectious diseases in the future.Methods　NESID and RTS data from March 2014 to October 2017 were obtained from the Kawasaki City Institute for Public Health. First, the operating methodologies of the two surveillance systems were compared. Second, RTS data were used to analyze the daily epidemic curve, and then the daily number of influenza cases was converted into weekly data for comparison with NESID data. Pearson's correlation coefficients and 95% confidence intervals (CIs) were calculated. Correlations were also analyzed after data for the last and first weeks of each year were excluded because few hospitals remain open around the New Year holiday, resulting in a disproportionately large number of patients visiting the few institutions that remain open.Results　The NESID relies on data provided by a fixed number of medical institutions determined each fiscal year (mean: 56.0±4.2 institutions), while the number of institutions providing data for the RTS varies daily or monthly. In September 2017, 691 of the 1,032 eligible institutions (67.0%) were registered for the RTS. Pearson's correlation coefficient for the two surveillance systems was 0.975 (95%CI, 0.967-0.981); when data for the last and first week of each year were excluded, it was 0.989 (95%CI 0.986-0.992). In each of the three seasons that were investigated, an increase in the incidence of type A influenza preceded an increase in the incidence of type B influenza.Conclusion　The operating methodologies of the two surveillance systems differed; however, the results identified a strong correlation, confirming the reliability of the RTS. The RTS collects daily data by influenza type; therefore, it detects epidemic onsets at an earlier stage, facilitating more detailed epidemiological analysis, compared with that of the NESID. It is necessary to understand differences in the characteristics between two surveillance systems when we analyze influenza surveillance data.

The influence of assumptions on generation time distributions in epidemic models

A simple class of stochastic models for epidemic spread in finite, but large, populations is studied. The purpose is to investigate how assumptions about the times between primary and secondary infections influences the outcome of the epidemic. Of particular interest is how assumptions of individual variability in infectiousness relates to variability of the epidemic curve. The main concern is the final size of the epidemic and the time scale at which it evolves. The theoretical results are illustrated by simulations.

Estimation of reproduction number and probable vector density of the first autochthonous dengue outbreak in Japan in the last 70 years

OBJECTIVES

The first autochthonous case of dengue fever in Japan since 1945 was reported on August 27, 2014. Infection was transmitted by Aedes albopictus mosquitoes in Tokyo's Yoyogi Park. A total of 65 cases with no history of overseas travel and who may have been infected around the park were reported as of September 5, 2014. To quantify infection risk of the local epidemic, the reproduction number and vector density per person at the onset of the epidemic were estimated.

METHODS

The estimated probability distribution and the number of female mosquitoes per person (MPP) were determined from the data of the initial epidemic.

RESULTS

The estimated distribution R(0i) for the initial epidemic was fitted to a Gamma distribution using location parameter 4.25, scale parameter 0.19, and shape parameter 7.76 with median 7.78 and IQR (7.21-8.40). The MPP was fitted to a normal distribution with mean 5.71 and standard deviation 0.53.

CONCLUSIONS

Both estimated reproduction number and vector density per person at the onset of the epidemic were higher than previously reported values. These results indicate the potential for dengue outbreaks in places with elevated vector density per person, even in dengue non-endemic countries. To investigate the cause of this outbreak, further studies will be needed, including assessments of social, behavioral, and environmental factors that may have contributed to this epidemic by altering host and vector conditions in the park.