Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics of Mycobacterium tuberculosis

Infectious Period ◽

Reproductive Number ◽

Data Sets ◽

Terminal Branch ◽

Branch Lengths ◽

Disease Epidemics ◽

Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether difference in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, and molecular clock), and found that all these factors, except the length of the infectious period and R0, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: 1) clustering results and TBL depend on many factors that have nothing to do with transmission, 2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.

Estimation of the basic reproductive number (R0) for epidemic, highly pathogenic avian influenza subtype H5N1 spread

Epidemiology and Infection ◽

10.1017/s0950268808000885 ◽

2008 ◽

Vol 137 (2) ◽

pp. 219-226 ◽

Cited By ~ 35

Author(s):

M. P. WARD ◽

D. MAFTEI ◽

C. APOSTU ◽

A. SURU

Keyword(s):

Avian Influenza ◽

Highly Pathogenic Avian Influenza ◽

Control Strategies ◽

Infectious Period ◽

Reproductive Number ◽

Highly Pathogenic ◽

Pathogenic Avian Influenza ◽

True Basic ◽

Road Distance

SUMMARYThree different methods were used for estimating the basic reproductive number (R0) from data on 110 outbreaks of highly pathogenic avian influenza (HPAI) subtype H5N1 that occurred in village poultry in Romania, 12 May to 6 June 2006. We assumed a village-level infectious period of 7 days. The methods applied were GIS-based identification of nearest infectious neighbour (based on either Euclidean or road distance), the method of epidemic doubling time, and a susceptible–infectious (SI) modelling approach. In general, the estimated basic reproductive numbers were consistent: 2·14, 1·95, 2·68 and 2·21, respectively. Although the true basic reproductive number in this epidemic is unknown, results suggest that the use of a range of methods might be useful for characterizing epidemics of infectious diseases. Once the basic reproductive number has been estimated, better control strategies and targeted surveillance programmes can be designed.

Universal scaling law for COVID-19 propagation in urban centers

10.1101/2020.06.22.20137604 ◽

2020 ◽

Author(s):

Ben-Hur Francisco Cardoso ◽

Sebastián Gonçalves

Keyword(s):

New York ◽

Population Density ◽

Urban Areas ◽

Transmission Rate ◽

Scaling Law ◽

City Size ◽

Reproductive Number ◽

Continuous Change ◽

Urban Centers

Due to the COVID-19 pandemic, there is a high demand for Susceptible-Infective-Recovered (SIR) models to adjust and predict the number of cases in urban areas. Forecasting, however, is a difficult task, because the change in people’s behavior reflects in a continuous change in the parameters of the model. An important question is what we can use from one city to another; if what happened in Madrid could have been applied to New York and then, if what we have learned from this city would be useful for São Paulo. To answer this question, we present an analysis of the transmission rate of COVID-19 as a function of population density and population size for US counties, cities of Brazil, German, and Portugal. Contrary to the common hypothesis in epidemics modeling, we observe a higher disease transmissibility for higher city’s population density/size –with the latter showing more predicting power. We present a contact rate scaling theory that explain the results, predicting that the basic reproductive number R0 of epidemics scales as the logarithm of the city size.

The effects of the clinical symptoms pneumonia-confirmation strategy of the COVID-19 epidemic in Wuhan, China

10.21203/rs.3.rs-28619/v1 ◽

2020 ◽

Author(s):

Yanjin Wang ◽

Pei Wang ◽

Shudao Zhang ◽

Hao Pan

Keyword(s):

Turning Point ◽

Clinical Symptoms ◽

Transmission Rate ◽

Reproductive Number ◽

The Novel ◽

Wuhan City ◽

Start Date ◽

Rapid Spread ◽

Novel Coronavirus

Abstract Motivated by the quick control in Wuhan, China, and the rapid spread in other countries of COVID-19, we investigate the questions that what is the turning point in Wuhan by quantifying the variety of basic reproductive number after the lockdown city. The answer may help the world to control the COVID-19 epidemic. A modified SEIR model is used to study the COVID-19 epidemic in Wuhan city. Our model is calibrated by the hospitalized cases. The modeling result gives out that the means of basic reproductive numbers are 1.5517 (95% CI 1.1716-4.4283) for the period from Jan 25 to Feb 11, 2020, and 0.4738(95% CI 0.0997-0.8370) for the period from Feb 12 to Mar 10. The transmission rate fell after Feb 12, 2020 as a result of China’s COVID-19 strategy of keeping society distance and the medical support from all China, but principally because of the clinical symptoms to be used for the novel coronavirus pneumonia (NCP) confirmation in Wuhan since Feb 12, 2020. Clinical diagnosis can quicken up NCP-confirmation such that the COVID-19 patients can be isolated without delay. So the clinical symptoms pneumonia-confirmation is the turning point of the COVID-19 battle of Wuhan. The measure of clinical symptoms pneumonia-confirmation in Wuhan has delayed the growth and reduced size of the COVID-19 epidemic, decreased the peak number of the hospitalized cases by 96% in Wuhan. Our modeling also indicates that the earliest start date of COVID-19 in Wuhan may be Nov 2, 2019.

An artificially simulated outbreak of a respiratory infectious disease

10.21203/rs.2.14142/v7 ◽

2020 ◽

Author(s):

Zuiyuan Guo ◽

Shuang Xu ◽

Libo Tong ◽

Botao Dai ◽

Yuandong Liu ◽

...

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Attack Rate ◽

General Pattern ◽

Reproductive Number ◽

Developmental Trend ◽

Collision Model ◽

Military Camp ◽

Abstract Background Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeded 1.5, the median attack rate increased sharply; when R 0 =3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusions The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.

An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis (Preprint)

10.2196/preprints.23624 ◽

2020 ◽

Author(s):

Motoaki Utamura ◽

Makoto Koizumi ◽

Seiichi Kirikami

Keyword(s):

Public Health ◽

Transmission Rate ◽

Time Lag ◽

Sir Model ◽

Epidemiological Model ◽

Polymerase Chain Reaction Test ◽

Series Data ◽

Reproductive Number ◽

Alternative Formulation

BACKGROUND COVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies. OBJECTIVE This study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count. METHODS Time-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model. RESULTS In a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag <i>T</i> from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of <i>T</i>=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19. CONCLUSIONS A novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

An artificially simulated outbreak of a respiratory infectious disease

10.21203/rs.2.14142/v5 ◽

2020 ◽

Author(s):

Zuiyuan Guo ◽

Shuang Xu ◽

Libo Tong ◽

Botao Dai ◽

Yuandong Liu ◽

...

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Attack Rate ◽

General Pattern ◽

Reproductive Number ◽

Developmental Trend ◽

Collision Model ◽

Military Camp ◽

Inference of seasonal and pandemic influenza transmission dynamics

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1415012112 ◽

2015 ◽

Vol 112 (9) ◽

pp. 2723-2728 ◽

Cited By ~ 79

Author(s):

Wan Yang ◽

Marc Lipsitch ◽

Jeffrey Shaman

Keyword(s):

The United States ◽

Transmission Dynamics ◽

Disease Spread ◽

Infectious Period ◽

Recent Emergence ◽

Influenza Transmission ◽

Inference Methods ◽

Epidemiological Characteristics ◽

Epidemiological Parameters

The inference of key infectious disease epidemiological parameters is critical for characterizing disease spread and devising prevention and containment measures. The recent emergence of surveillance records mined from big data such as health-related online queries and social media, as well as model inference methods, permits the development of new methodologies for more comprehensive estimation of these parameters. We use such data in conjunction with Bayesian inference methods to study the transmission dynamics of influenza. We simultaneously estimate key epidemiological parameters, including population susceptibility, the basic reproductive number, attack rate, and infectious period, for 115 cities during the 2003–2004 through 2012–2013 seasons, including the 2009 pandemic. These estimates discriminate key differences in the epidemiological characteristics of these outbreaks across 10 y, as well as spatial variations of influenza transmission dynamics among subpopulations in the United States. In addition, the inference methods appear to compensate for observational biases and underreporting inherent in the surveillance data.

Transmission dynamics of SARS-CoV-2 in a mid-size city of China

BMC Infectious Diseases ◽

10.1186/s12879-021-06522-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Hongjun Zhao ◽

Xiaoxiao Lu ◽

Wenhui Lun ◽

Tiegang Li ◽

Boqi Rao ◽

...

Keyword(s):

Case Series ◽

Reproductive Number ◽

Wuhan City ◽

The Public ◽

Global Pandemic ◽

Asymptomatic Patients ◽

History Of ◽

Multifaceted Interventions ◽

Abstract Background An outbreak of pneumonia, COVID-19 associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan city and then rapidly spread to other cities. Wenzhou is located approximately 900 km from Wuhan, which was experiencing an outbreak that was severe at the time but is considered modest as the epidemic became a pandemic. We described the epidemiological characteristics of SARS-CoV-2 outside of the epicenter to help understand the transmission pattern in a mid-sized Chinese city. Methods To investigate the epidemiological and clinical characteristics of the COVID-19, we described case series of 473 patients with confirmed COVID-19 in Wenzhou, China from January 27 to March 16, 2020. We described the public health interventions of COVID-19 and evaluated the effect of interventions by the effective reproduction number (Rt). Results The median age of all patients was 47.6 years, 48.4% of whom were female. 33.8% of the patients had a history of residence in Wuhan. Fever (71.7%) and cough (43.1%) were the most common symptoms. In addition, three kinds of unconventional cases were observed, namely 4.9% asymptomatic patients, 7.6% confirmed patients who had no link to Wuhan city but contact with individuals from Wuhan without any symptoms at the time of contact, and 12.9% confirmed patients who had an unknown source of transmission. We estimated that the basic reproductive number (R0) was 2.75 (95% CI: 2.37–3.23). The Rt fluctuated within the range of 2.50 to 3.74 from January 11 to January 16 while gradually reached a peak of 3.74 on January 16. Rt gradually decreased after January 16 and decreased to 1.00 on January 30. Rt continually decreased and reached the lowest point (0.03) on February 21, 2020. Conclusion Our study presented the possibility of asymptomatic carriers affected with SARS-CoV-2, and transmission by these three kinds of unconventional patients in Wenzhou may be an important characteristic of SARS-CoV-2 transmission. The evaluation showed that a series of multifaceted interventions proved effective in controlling the epidemic of COVID-19. These findings might provide valuable examples of control policies for countries or areas in combatting the global pandemic of COVID-19.

An artificially simulated outbreak of a respiratory infectious disease

10.21203/rs.2.14142/v1 ◽

2019 ◽

Author(s):

Zuiyuan Guo ◽

Shuang Xu ◽

Libo Tong ◽

Botao Dai ◽

Yuandong Liu

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Attack Rate ◽

General Pattern ◽

Reproductive Number ◽

Developmental Trend ◽

Collision Model ◽

Military Camp ◽

Abstract Background Outbreaks of respiratory infectious diseases often take place in crowded places. To understand the spreading pattern of an outbreak of a respiratory infectious disease and provide a theoretical basis for the targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between the onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeds 1.5, the median attack rate increases sharply; when R 0 =3, with a delay in the TOI, the attack rate increases gradually and eventually remains stable. If the IOI exceeds 2.3 days, the median attack rate will also increase dramatically. If the IR exceeds 0.5, the median of the attack rate nears zero. The median generation time was 8.26 days (95% CI: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and the R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusion The random collision model not only simulates how an epidemic spreads with superior precision but also allows more flexibility in the settings of the exposure population’s activities and different types of infectious diseases, which is conducive to furthering the exploration of the epidemiological characteristics of epidemic outbreaks.

Population structure, biogeography and transmissibility of Mycobacterium tuberculosis

Nature Communications ◽

10.1038/s41467-021-26248-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Luca Freschi ◽

Roger Vargas ◽

Ashaque Husain ◽

S. M. Mostofa Kamal ◽

Alena Skrahina ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Population Structure ◽

Global Scale ◽

Rapid Identification ◽

Genomic Diversity ◽

Terminal Branch ◽

Nucleotide Substitutions ◽

Central Asian ◽

Depth Analysis ◽

Branch Lengths

AbstractMycobacterium tuberculosis is a clonal pathogen proposed to have co-evolved with its human host for millennia, yet our understanding of its genomic diversity and biogeography remains incomplete. Here we use a combination of phylogenetics and dimensionality reduction to reevaluate the population structure of M. tuberculosis, providing an in-depth analysis of the ancient Indo-Oceanic Lineage 1 and the modern Central Asian Lineage 3, and expanding our understanding of Lineages 2 and 4. We assess sub-lineages using genomic sequences from 4939 pan-susceptible strains, and find 30 new genetically distinct clades that we validate in a dataset of 4645 independent isolates. We find a consistent geographically restricted or unrestricted pattern for 20 groups, including three groups of Lineage 1. The distribution of terminal branch lengths across the M. tuberculosis phylogeny supports the hypothesis of a higher transmissibility of Lineages 2 and 4, in comparison with Lineages 3 and 1, on a global scale. We define an expanded barcode of 95 single nucleotide substitutions that allows rapid identification of 69 M. tuberculosis sub-lineages and 26 additional internal groups. Our results paint a higher resolution picture of the M. tuberculosis phylogeny and biogeography.