The association between insulin resistance and depressive symptoms - A national representative cross-sectional study.

OBJECTIVE: Increasing evidence suggests a positive association between insulin resistance (IR) and depression. However, whether sex-or body mass index-specific differences exist remains controversial, and only few studies have analyzed specific symptom domains. Thus, the present study aimed to analyze the association between IR and depressive symptom domains and to clarify the effects of sex and body mass index. METHODS: The study sample comprised 4007 participants, aged 19-79, from the Korea National Health and Nutrition Examination Study 2020. Participants completed health interviews and examinations, providing data on circulating insulin and glucose levels, the Patient Health Questionnaire-9 (PHQ-9), and related covariates. IR was calculated using the homeostasis model assessment of insulin resistance. Associations between IR and PHQ-9 were analyzed using negative binomial regression with adjustments for the complex survey design. RESULTS: The association between log-transformed IR and PHQ-9 total scores was statistically significant (incidence rate ratio [IRR] = 1.17, 95% confidence interval [CI] = 1.07-1.29, p = 0.001). Only body mass index specific differences were statistically significant, as the association was only significant in those without obesity (IRR = 1.21, 95% CI = 1.06-1.38, p = 0.005). IR was associated with cognitive/affective (IRR = 1.23, 95% CI = 1.08-1.41, p = 0.002) and somatic (IRR = 1.14, 95% CI = 1.04-1.25, p = 0.005) depressive symptom domains. Sensitivity analyses revealed similar results. CONCLUSIONS: IR was positively associated with cognitive/affective and somatic depressive symptoms in non-obese individuals.

[The Validity of the Poisson Distribution to Analyze Microbial Colony Counts on Agar Plates for Food Samples].

Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.

Short-term effects of air pollution on hospital admissions of respiratory diseases in Hamadan, Iran, 2015 to 2021.

The short-term effects of air pollution on respiratory diseases have been reported in many countries. Urban areas are most affected because of the many sources of pollution and the large number of people living there. This study aims to investigate the effect of short-term exposure to air pollutants on respiratory hospital admissions in the city of Hamadan. In this ecological study, daily hospital admission data were collected from Shahid Beheshti Hospital in Hamadan. Daily information on air pollutants (CO, SO2, NO2, O3, PM2.5 and PM10) from Hamadan Department of Environment (DoE) organization and of climate factors from Hamadan Meteorological Office were collected. A negative binomial regression model was used to examine the effect of air pollution on daily respiratory hospitalizations. The effect of exposure to pollutants was measured whit different time lags (0-7 days). Furthermore, the effect of meteorological variables was controlled. Subgroup analyses were performed by sex and age group. A total of 12,454 hospitalizations for respiratory diseases were recorded. Results showed a strong and immediate effect of CO on respiratory hospital admissions with highest association at lag 7 (relative risk (RR) = 1.38, 95% CI: 1.33, 1.42). The effects of CO and SO2 on respiratory hospitalizations are greater for men than women. Regarding the short-term effects of PM2.5, SO2 and O3, adults (aged less than 65) were more prone to hospitalization for respiratory diseases. These results show that exposure to air pollution, particularly CO, may increase hospital admissions due to respiratory illness. So reducing the concentration of these pollutants can reduce the number of hospital admissions.

DELFMUT: duplex sequencing-oriented depth estimation model for stable detection of low-frequency mutations.

Duplex sequencing technology has been widely used in the detection of low-frequency mutations in circulating tumor deoxyribonucleic acid (DNA), but how to determine the sequencing depth and other experimental parameters to ensure the stable detection of low-frequency mutations is still an urgent problem to be solved. The mutation detection rules of duplex sequencing constrain not only the number of mutated templates but also the number of mutation-supportive reads corresponding to each forward and reverse strand of the mutated templates. To tackle this problem, we proposed a Depth Estimation model for stable detection of Low-Frequency MUTations in duplex sequencing (DELFMUT), which models the identity correspondence and quantitative relationships between templates and reads using the zero-truncated negative binomial distribution without considering the sequences composed of bases. The results of DELFMUT were verified by real duplex sequencing data. In the case of known mutation frequency and mutation detection rule, DELFMUT can recommend the combinations of DNA input and sequencing depth to guarantee the stable detection of mutations, and it has a great application value in guiding the experimental parameter setting of duplex sequencing technology.

Risk factors identification of COVID-19 patients with chronic obstructive pulmonary disease: A retrospective study in Punjab-Pakistan.

BACKGROUND: Accessibility to the immense collection of studies on noncommunicable diseases related to coronavirus disease of 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an immediate focus of researchers. However, there is a scarcity of information about chronic obstructed pulmonary disease (COPD), which is associated with a high rate of infection in COVID-19 patients. Moreover, by combining the effects of the SARS-CoV-2 on COPD patients, we may be able to overcome formidable obstacles factors, and diagnosis influencers. MATERIALS AND METHODS: A retrospective study of 280 patients was conducted at DHQ Hospital Muzaffargarh in Punjab, Pakistan. Negative binomial regression describes the risk of fixed successive variables. The association is described by the Cox proportional hazard model and the model coefficient is determined through log-likelihood observation. Patients with COPD had their survival and mortality plotted on Kaplan-Meier curves. RESULTS: The increased risk of death in COPD patients was due to the effects of variables such as cough, lower respiratory tract infection (LRTI), tuberculosis (TB), and body-aches being 1.369, 0.693, 0.170, and 0.217 times higher at (95% confidence interval [CI]: 0.747-1.992), (95% CI: 0.231-1.156), (95% CI: 0.008-0.332), and (95% CI: -0.07 to 0.440) while it decreased 0.396 in normal condition. CONCLUSION: We found that the symptoms of COPD (cough, LRTI, TB, and bodyaches) are statistically significant in patients who were most infected by SARS-CoV-2.

Beta-negative binomial nonlinear spatio-temporal random effects modeling of COVID-19 case counts in Japan.

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach. This paper describes spatio-temporal modeling of COVID-19 case counts in 47 prefectures of Japan using a nonlinear random effects model, where random effects are introduced to capture the heterogeneity of a number of model parameters associated with the prefectures. The negative binomial distribution is frequently used with the Paul-Held random effects model to account for overdispersion in count data; however, the negative binomial distribution is known to be incapable of accommodating extreme observations such as those found in the COVID-19 case count data. We therefore propose use of the beta-negative binomial distribution with the Paul-Held model. This distribution is a generalization of the negative binomial distribution that has attracted much attention in recent years because it can model extreme observations with analytical tractability. The proposed beta-negative binomial model was applied to multivariate count time series data of COVID-19 cases in the 47 prefectures of Japan. Evaluation by one-step-ahead prediction showed that the proposed model can accommodate extreme observations without sacrificing predictive performance.

Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data.

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.

Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system.

Many statistical methods have been applied to VAERS (vaccine adverse event reporting system) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. The AE ontology contains important information about biological similarities between AEs. In this paper, we develop a model to estimate vaccine-AE associations while incorporating the AE ontology. We model a group of AEs using the zero-inflated negative binomial model and then estimate the vaccine-AE association using the empirical Bayes approach. This model handles the AE count data with excess zeros and allows borrowing information from related AEs. The proposed approach was evaluated by simulation studies and was further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS) dataset. The proposed method is implemented in an R package available at https://github.com/umich-biostatistics/zGPS.AO.

Does illicit amphetamine seizures quantity associated with amphetamine use disorder related admissions in Saudi Arabia?

BACKGROUND: Illicit amphetamine-type stimulants (ATS) trafficking activities have increased substantially in Saudi Arabia over the last 10 years. In the period 2013-2017 Saudi Arabia seized the largest quantities of amphetamine at the global level. The current study examines whether the increased quantity of ATS seizures has an impact on amphetamine use disorder admissions. METHOD: This is an ecological study combining two datasets, the first dataset was obtained from United Nations Office on Drugs and Crime (UNODC), and the Al-Amal Hospital Electronic Health Record System in the city of Dammam, Eastern region of Saudi Arabia from 2005 to 2018. The annual incidence of patients diagnosed with amphetamine use was the dependent variable. The independent variable was the annual reported count of seized quantities of ATS in Saudi Arabia. We used a random intercept Negative Binomial model to predict the yearly count of amphetamine use disorder admission rates. RESULTS: A total of 910 amphetamine disorder admission patients in Al-Amal rehabilitation and addiction center, and the quantity equivalent to 200 tons of ATS was seized from 2005 to 2018. The amphetamine disorder admission rate has increased from 1.33% in 2005 to 18.27% in 2018. For each one-unit increase in the amphetamine confiscated quantities, the amphetamine use disorder admission rate increased by 49 to 88%. CONCLUSION: The current study found that reported amphetamine seized quantities were significantly and positively associated with the increase of amphetamine use disorder-related admission rates. In 2018, both ATS seized quantities and admission rates significantly increased, nearly doubling from the previous year. Rigorous, and multidisciplinary interventional studies to evaluate factors associated with increasing abuse of ATS should be a priority for policymakers and researchers in Saudi.

Spatial distribution of Echinolitorina peruviana (Lamarck, 1882) for intertidal rocky shore in Antofagasta (23° S, Chile) / Distribuição espacial de Echinolitorina peruviana (Lamarck, 1882) para costa rochosa entremarés em Antofagasta (23 ° S, Chile)

The intertidal rocky shores in continental Chile have high species diversity mainly in northern Chile (18-27° S), and one of the most widespread species is the gastropod Echinolittorina peruviana (Lamarck, 1822). The aim of the present study is do a first characterization of spatial distribution of E. peruviana in along rocky shore in Antofagasta town in northern Chile. Individuals were counted in nine different sites that also were determined their spectral properties using remote sensing techniques (LANDSAT ETM+). The results revealed that sites without marked human intervention have more abundant in comparison to sites located in the town, also in all studied sites was found an aggregated pattern, and in six of these sites were found a negative binomial distribution. The low density related to sites with human intervention is supported when spectral properties for sites were included. These results would agree with other similar results for rocky shore in northern and southern Chile.

As costas rochosas entremarés no Chile continental apresentam alta diversidade de espécies, principalmente no norte do país (18-27 ° S), e uma das espécies mais difundidas é o gastrópode Echinolittorina peruviana (Lamarck, 1822). O objetivo do presente estudo é fazer uma primeira caracterização da distribuição espacial de E. peruviana no costão rochoso da cidade de Antofagasta no norte do Chile. Os indivíduos foram contados em nove locais diferentes onde também foram determinadas suas propriedades espectrais usando técnicas de sensoriamento remoto (LANDSAT ETM +). Os resultados revelaram que os locais sem intervenção humana marcada apresentam maior abundância em comparação aos locais localizados no município. Também em todos os locais estudados foi encontrado um padrão agregado, sendo que em seis desses locais foi encontrada uma distribuição binomial negativa. A baixa densidade relacionada a sites com intervenção humana é suportada quando as propriedades espectrais para sites foram incluídas. Esses resultados concordariam com outros resultados semelhantes para costões rochosos no norte e no sul do Chile.

Spatial distribution of Echinolitorina peruviana (Lamarck, 1882) for intertidal rocky shore in Antofagasta (23° S, Chile)

Abstract The intertidal rocky shores in continental Chile have high species diversity mainly in northern Chile (18-27° S), and one of the most widespread species is the gastropod Echinolittorina peruviana (Lamarck, 1822). The aim of the present study is do a first characterization of spatial distribution of E. peruviana in along rocky shore in Antofagasta town in northern Chile. Individuals were counted in nine different sites that also were determined their spectral properties using remote sensing techniques (LANDSAT ETM+). The results revealed that sites without marked human intervention have more abundant in comparison to sites located in the town, also in all studied sites was found an aggregated pattern, and in six of these sites were found a negative binomial distribution. The low density related to sites with human intervention is supported when spectral properties for sites were included. These results would agree with other similar results for rocky shore in northern and southern Chile.

Resumo As costas rochosas entremarés no Chile continental apresentam alta diversidade de espécies, principalmente no norte do país (18-27 ° S), e uma das espécies mais difundidas é o gastrópode Echinolittorina peruviana (Lamarck, 1822). O objetivo do presente estudo é fazer uma primeira caracterização da distribuição espacial de E. peruviana no costão rochoso da cidade de Antofagasta no norte do Chile. Os indivíduos foram contados em nove locais diferentes onde também foram determinadas suas propriedades espectrais usando técnicas de sensoriamento remoto (LANDSAT ETM +). Os resultados revelaram que os locais sem intervenção humana marcada apresentam maior abundância em comparação aos locais localizados no município. Também em todos os locais estudados foi encontrado um padrão agregado, sendo que em seis desses locais foi encontrada uma distribuição binomial negativa. A baixa densidade relacionada a sites com intervenção humana é suportada quando as propriedades espectrais para sites foram incluídas. Esses resultados concordariam com outros resultados semelhantes para costões rochosos no norte e no sul do Chile.

Spatial distribution of Echinolitorina peruviana (Lamarck, 1882) for intertidal rocky shore in Antofagasta (23° S, Chile) / Distribuição espacial de Echinolitorina peruviana (Lamarck, 1882) para costa rochosa entremarés em Antofagasta (23 ° S, Chile)

Abstract The intertidal rocky shores in continental Chile have high species diversity mainly in northern Chile (18-27° S), and one of the most widespread species is the gastropod Echinolittorina peruviana (Lamarck, 1822). The aim of the present study is do a first characterization of spatial distribution of E. peruviana in along rocky shore in Antofagasta town in northern Chile. Individuals were counted in nine different sites that also were determined their spectral properties using remote sensing techniques (LANDSAT ETM+). The results revealed that sites without marked human intervention have more abundant in comparison to sites located in the town, also in all studied sites was found an aggregated pattern, and in six of these sites were found a negative binomial distribution. The low density related to sites with human intervention is supported when spectral properties for sites were included. These results would agree with other similar results for rocky shore in northern and southern Chile.

Resumo As costas rochosas entremarés no Chile continental apresentam alta diversidade de espécies, principalmente no norte do país (18-27 ° S), e uma das espécies mais difundidas é o gastrópode Echinolittorina peruviana (Lamarck, 1822). O objetivo do presente estudo é fazer uma primeira caracterização da distribuição espacial de E. peruviana no costão rochoso da cidade de Antofagasta no norte do Chile. Os indivíduos foram contados em nove locais diferentes onde também foram determinadas suas propriedades espectrais usando técnicas de sensoriamento remoto (LANDSAT ETM +). Os resultados revelaram que os locais sem intervenção humana marcada apresentam maior abundância em comparação aos locais localizados no município. Também em todos os locais estudados foi encontrado um padrão agregado, sendo que em seis desses locais foi encontrada uma distribuição binomial negativa. A baixa densidade relacionada a sites com intervenção humana é suportada quando as propriedades espectrais para sites foram incluídas. Esses resultados concordariam com outros resultados semelhantes para costões rochosos no norte e no sul do Chile.

What can occupancy models gain from time-to-detection data?

The time taken to detect a species during site occupancy surveys contains information about the observation process. Accounting for the observation process leads to better inference about site occupancy. We explore the gain in efficiency that can be obtained from time-to-detection (TTD) data and show that this model type has a significant benefit for estimating the parameters related to detection intensity. However, for estimating occupancy probability parameters, the efficiency improvement is generally very minor. To explore whether TTD data could add valuable information when detection intensities vary between sites and surveys, we developed a mixed exponential TTD occupancy model. This new model can simultaneously estimate the detection intensity and aggregation parameters when the number of detectable individuals at the site follows a negative binomial distribution. We found that this model provided a much better description of the occupancy patterns than conventional detection/nondetection methods among 63 bird species data from the Karoo region of South Africa. Ignoring the heterogeneity of detection intensity in the TTD model generally yielded a negative bias in the estimated occupancy probability. Using simulations, we briefly explore study design trade offs between numbers of sites and surveys for different occupancy modeling strategies.

Comparisons of statistical distributions for cluster sizes in a developing pandemic.

BACKGROUND: We consider cluster size data of SARS-CoV-2 transmissions for a number of different settings from recently published data. The statistical characteristics of superspreading events are commonly described by fitting a negative binomial distribution to secondary infection and cluster size data as an alternative to the Poisson distribution as it is a longer tailed distribution, with emphasis given to the value of the extra parameter which allows the variance to be greater than the mean. Here we investigate whether other long tailed distributions from more general extended Poisson process modelling can better describe the distribution of cluster sizes for SARS-CoV-2 transmissions. METHODS: We use the extended Poisson process modelling (EPPM) approach with nested sets of models that include the Poisson and negative binomial distributions to assess the adequacy of models based on these standard distributions for the data considered. RESULTS: We confirm the inadequacy of the Poisson distribution in most cases, and demonstrate the inadequacy of the negative binomial distribution in some cases. CONCLUSIONS: The probability of a superspreading event may be underestimated by use of the negative binomial distribution as much larger tail probabilities are indicated by EPPM distributions than negative binomial alternatives. We show that the large shared accommodation, meal and work settings, of the settings considered, have the potential for more severe superspreading events than would be predicted by a negative binomial distribution. Therefore public health efforts to prevent transmission in such settings should be prioritised.

An Accelerated Life Model Analog for Discrete Survival and Count Data.

BACKGROUND AND OBJECTIVE: Our goal is to provide an overall strategy for utilizing continuous accelerated life models in the discrete setting that provides a unique and flexible modeling approach across a variety of hazard shapes. METHODS: We convert well-known continuous accelerated life distributions into their discrete counterpart and show theoretically that the existing software that currently exists to accommodate, left, right and interval censoring in the continuous case is re-usable in the discrete setting due to the structure of the likelihood equations. RESULTS: We demonstrate across a variety of simulated and real-world data that our modeling approach can accommodate discrete data that may either be approximately symmetric, left-skewed or right skewed, overcoming the limitations of more traditional modeling approaches. CONCLUSIONS: We illustrate both theoretically and through simulations that our approach for accommodating discrete failure time and count data is quite flexible. We demonstrate that the special case of the discrete Weibull model readily can accommodate truly Poisson distributed data and has a great degree of flexibility for non-Poisson distributed data.

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty.

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 105 and the growth kinetic data of Listeria monocytogenes with an initial cell count of 104. The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 101, 102, and 103 and growth with average of initial cell numbers of 10-1, 100, and 101. The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 101 were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.

Sand Fly Fauna, Spatial Distribution of Lutzomyia longipalpis (Diptera: Psychodidae), and Climate Factors in Dourados, Brazil.

Studies of the geographic distribution of sand flies and the factors associated with their occurrence are necessary to understand the risk of leishmaniasis transmission. The objective of this study was to characterize the sand fly fauna, particularly the spatial distribution of Lutzomyia longipalpis (Lutz & Neiva), and correlate these with climate factors in the Dourados municipality, Brazil. The collection of sand flies was carried out with CDC Light Traps over two periods: at six sites for three consecutive nights each month from August 2012 to July 2013; and at four other sites for two consecutive nights each month from April 2017 to February 2018. We collected 591 sand flies in the first period and 121 in the second period for a total of 712 sand flies; 697 of the total collected were Lu. longipalpis. The minimum and maximum sand fly infestation rate (sites with vector presence) was 11.1% and 83.33% in the first period, and 0% and 50.0% in the second period. No sand flies with Leishmania were identified via PCR. Lu. longipalpis presented an aggregate disposition with excellent adjustment. Rainfall and relative humidity were the abiotic factors that influenced the vector infestation level. The aggregate distribution for this species was predicted by the environmental factors that favor the proliferation of Lu. longipalpis. The results of this study should assist in devising measures to control sand flies in Dourados, Brazil.

Multiple paternity and the number of offspring: A model reveals two major groups of species.

Parentage analyses via microsatellite markers have revealed multiple paternity within the broods of polytocous species of mammals, reptiles, amphibians, fishes and invertebrates. The widespread phenomenon of multiple paternity may have attending relationships with such evolutionary processes as sexual selection and kin selection. However, just how much multiple paternity should a species exhibit? We developed Bayesian null models of how multiple paternity relates to brood sizes. For each of 114 species with published data on brood sizes and numbers of sires, we compared our null model estimates to published frequencies of multiple paternity. The majority of species fell close to our null model, especially among fish and invertebrate species. Some species, however, had low probabilities of multiple paternity, far from the predictions of the null model, likely due to sexual selection and environmental constraints. We suggest a major division among species' mating systems between those with close to random mating and high levels of multiple paternity, and those with constraints that produce low levels of multiple paternity.

Accurate estimation for extra-Poisson variability assuming random effect models.

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.

Count regression models for COVID-19.

At the end of 2019, the current novel coronavirus emerged as a severe acute respiratory disease that has now become a worldwide pandemic. Future generations will look back on this difficult period and see how our society as a whole united and rose to this challenge. Many reports have suggested that this new virus is becoming comparable to the Spanish flu pandemic of 1918. We provide a statistical study on the modelling and analysis of the daily incidence of COVID-19 in eighteen countries around the world. In particular, we investigate whether it is possible to fit count regression models to the number of daily new cases of COVID-19 in various countries and make short term predictions of these numbers. The results suggest that the biggest advantage of these methods is that they are simplistic and straightforward allowing us to obtain preliminary results and an overall picture of the trends in the daily confirmed cases of COVID-19 around the world. The best fitting count regression model for modelling the number of new daily COVID-19 cases of all countries analysed was shown to be a negative binomial distribution with log link function. Whilst the results cannot solely be used to determine and influence policy decisions, they provide an alternative to more specialised epidemiological models and can help to support or contradict results obtained from other analysis.