scholarly journals Trend Analysis and Predictions of Coronavirus Disease 2019 in Ethiopia

2021 ◽  
Vol 21 (3) ◽  
pp. e00523-e00523
Author(s):  
Abiyot Negash Terefe ◽  
Samuel Getachew Zewudie
Keyword(s):  
Time Series ◽  
Disease Transmission ◽  
Global Economy ◽  
Negative Binomial ◽  
Moving Average ◽  
Negative Binomial Regression ◽  
Epidemic Disease ◽  
Binomial Regression ◽  
Total Death ◽  
Auto Regressive

Background: Coronavirus Disease 2019 (COVID-19) is affecting both lives of millions of people and the global economy of the world day by day. This study aimed to determine the trend of COVID-19 and its predictions in Ethiopia. Study Design: This study was conducted based on a time series design. Methods: The required data were collected from the Ethiopian COVID-19 monitoring platform beginning from the onset of the disease in the country until March 28, 2021. Furthermore, the auto-regressive integrated moving average models were used on daily-based time series. The Poisson and Negative Binomial regression were also employed to notice the effects of months on the transmission and disease-related human deaths. Results: The mean daily infection and death of COVID-19 in Ethiopia were 533.47±466.62 and 7.45±6.72, respectively. The peaks of infection and deaths in this country were in March, 2021, and August, 2020. In addition, the trend of daily new deaths (P=0.000) and infection (P=0.000) was significantly increasing. It is expected that around 10 million (8.6%) and 138,084.64 (0.12%) Ethiopians will be infected and die, respectively. Conclusions: The disease transmission and deaths vary from day to day and month to month. The highest peaks of COVID-19 infection and death were in March 2021 and August 2020. For the next end of August 2021, the COVID-19 daily new infection, new death, total case, and total death are expected to be increased. If this epidemic disease is not controlled, Ethiopia will face a severe shortage of hospitals, and the outbreak even becomes worse.

scholarly journals A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa

2019 ◽  
Vol 16 (11) ◽  
pp. 2000 ◽  
Author(s):  
Gbenga J. Abiodun ◽  
Olusola S. Makinde ◽  
Abiodun M. Adeola ◽  
Kevin Y. Njabo ◽  
Peter J. Witbooi ◽  
...  
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Model ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Negative Binomial Regression ◽  
Limpopo Province ◽  
Negative Binomial Regression Model ◽  
Climate Variables ◽  
Binomial Regression

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box–Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box–Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box–Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe―two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

Managing Threats on the High Seas: The Role of Naval Bases on Reducing Maritime Piracy

2021 ◽  
pp. 0095327X2110494
Author(s):  
Orlandrew E. Danzell ◽  
Jacob A. Mauslein ◽  
John D. Avelar
Keyword(s):  
Global Economy ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Security Issue ◽  
Maritime Piracy ◽  
Time Series Dataset ◽  
Binomial Regression ◽  
The Impact

Weak coastal states often lack an adequate, sustained naval presence to monitor and police their territorial waters. Unpatrolled waters, both territorial and otherwise, may provide pirates with substantial financial opportunities that go far beyond any single country. Maritime piracy costs the global economy on average USD 24 billion per year. This research explores the impact of naval bases on acts of piracy to determine if naval presence can decrease the likelihood of piracy. To examine this important economic and national security issue, our research employs a zero-inflated negative binomial regression model. We also rely upon a newly constructed time-series dataset for the years 1992–2018. Our study shows that the presence of naval bases is essential in helping maritime forces combat piracy. Policymakers searching for options to combat piracy should find the results of this study especially useful in creating prescriptive approaches that aid in solving offshore problems.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods: Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms.Results: Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001).Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods : Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms. Results : Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001). Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

Effect of Maximum Average Temperature on Suicide Rates in California, 2008-2017: Public Health Implications of Climate Change

2020 ◽  
Author(s):  
Mavra Qamar ◽  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie Nanos ◽  
David N Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Time Series ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Maximum Temperature ◽  
Suicide Rates ◽  
Binomial Regression ◽  
The Relationship ◽  
Heat Events

Abstract BackgroundSuicide prevention is a salient public health responsibility, as it is one of the top ten leading causes of premature mortality in the United States. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. Previous studies have been country-based. Currently, studies focused solely on regions, provinces, or states, such as California, are limited. The present study holds two purposes: i) to assess the effect of maximum temperature on suicides, and ii) to evaluate the effect of number of monthly heat events on suicide rates, in California from 2008-2017.MethodsThe exposure was measured as the average Californian daily maximum temperature within each month, and the number of monthly heat events, which was calculated as a count of the days exhibiting a >15% increase from the historical monthly temperature. The outcome was measured as California’s monthly suicide rate. Negative binomial regression models assessed the relationship between maximum temperature and suicides, and heat events and suicide. A seasonal decomposition of a time series and auto-correlogram further analyzed the seasonality of suicide and the trend from 2008-2017. ResultsThere were 40,315 deaths by suicide in California between 2008-2017. Negative binomial regression indicated a 6.1% increase in suicide incidence rate ratio (IRR) per 10°F increase in maximum temperature (IRR=1.00590 per 1°F, 95% CI: 1.00387, 1.00793, p<0.0001) and a positive, non-significant association between suicide rates and number of heat events adjusted for month of occurrence (IRR 1.00148 per heat event, 95% CI: 0.99636, 1.00661, p=0.572). The time series analysis and auto-correlogram suggested seasonality of deaths by suicide.ConclusionThe present study provided preliminary evidence that will generate future directions for research. We must seek to further illuminate the relationship of interest and apply our findings to public health interventions that will lower the rates of death by suicide as we are confronted with the effects of climate change.

Predicting Dengue Hemorraghic Fever Cases in Metro Manila, Philippines

2020 ◽  
Author(s):  
Imee Necesito ◽  
Jaewon Jung ◽  
Young Hye Bae ◽  
Soojun Kim ◽  
Hung Soo Kim
Keyword(s):  
Principal Components ◽  
Negative Binomial ◽  
Moving Average ◽  
Principal Component ◽  
Negative Binomial Regression ◽  
The Philippines ◽  
Dengue Case ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Metro Manila

&lt;p&gt;Researchers have been looking for methods to prevent, control and provide lifelong protection to humans against dengue disease which is brought by the dengue-carrying mosquito called the Aedes Aegypti. However, such prevention, control and protection will best be aided by a dengue case prediction model. This study used the Negative Binomial Regression to forecast the dengue case incidence in Metro Manila, Philippines using principal components as explanatory variables. To ensure that the dengue cases are predictable, close returns plot (CRP) was performed.&amp;#160;&amp;#160; The logarithm of dengue case incidence which were assigned as response variables have showed higher value of variance over the mean which validates the use of negative binomial regression. Principal Component Analysis utilizing Nino 3.4 sea surface temperature (SST), precipitation and minimum temperature was used in the study. The acquired principal components (PC1, PC2, PC3 and PC4) were used as the explanatory variables for the negative binomial regression to calculate the number of the logarithm of dengue case incidence. However, to improve the calculated value of DHF cases in comparison to its actual value, residuals from the negative binomial regression were treated using moving average approach. The data used in this study were from 1994-2010 climatological data. Results for both negative binomial and moving average were combined to get the forecasted dengue incidence. Forecasted values showed a maximum of 12% difference from the actual DHF cases indicating a high forecasting performance. This study which focused on predicting the possible dengue incidence in the central districts of the Philippines&amp;#160; is believed to be essential to create plans of action to prevent and control this disease.&lt;/p&gt;

scholarly journals Trends in inpatient care for psychiatric disorders in NHS hospitals across England, 1998/99–2019/20: an observational time series analysis

2021 ◽  
Author(s):  
Michelle Degli Esposti ◽  
Hisham Ziauddeen ◽  
Lucy Bowes ◽  
Aaron Reeves ◽  
Adam M. Chekroud ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Psychiatric Disorders ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Admission Rates ◽  
Series Analysis ◽  
Binomial Regression ◽  
Bed Days

Abstract Purpose It is unclear how hospitals are responding to the mental health needs of the population in England, against a backdrop of diminishing resources. We aimed to document patterns in hospital activity by psychiatric disorder and how these have changed over the last 22 years. Methods In this observational time series analysis, we used routinely collected data on all NHS hospitals in England from 1998/99 to 2019/20. Trends in hospital admissions and bed days for psychiatric disorders were smoothed using negative binomial regression models with year as the exposure and rates (per 1000 person-years) as the outcome. When linear trends were not appropriate, we fitted segmented negative binomial regression models with one change-point. We stratified by gender and age group [children (0–14 years); adults (15 years +)]. Results Hospital admission rates and bed days for all psychiatric disorders decreased by 28.4 and 38.3%, respectively. Trends were not uniform across psychiatric disorders or age groups. Admission rates mainly decreased over time, except for anxiety and eating disorders which doubled over the 22-year period, significantly increasing by 2.9% (AAPC = 2.88; 95% CI: 2.61–3.16; p < 0.001) and 3.4% (AAPC = 3.44; 95% CI: 3.04–3.85; p < 0.001) each year. Inpatient hospital activity among children showed more increasing and pronounced trends than adults, including an increase of 212.9% for depression, despite a 63.8% reduction for adults with depression during the same period. Conclusion In the last 22 years, there have been overall reductions in hospital activity for psychiatric disorders. However, some disorders showed pronounced increases, pointing to areas of growing need for inpatient psychiatric care, especially among children.

scholarly journals Examining the Association between the COVID-19 Pandemic and Self-Harm Death Counts in Four Canadian Provinces

2021 ◽  
Author(s):  
Shelly Isnar ◽  
Mark Oremus
Keyword(s):  
Ecological Study ◽  
Negative Binomial ◽  
Moving Average ◽  
Negative Binomial Regression ◽  
Income Loss ◽  
Autoregressive Integrated Moving Average ◽  
Binomial Regression ◽  
Self Harm ◽  
Ecological Associations ◽  
Canadian Provinces

Governments implemented lockdowns and other physical distancing measures to stop the spread of SARS-CoV-2 (COVID-19). Resulting unemployment, income loss, poverty, and social isolation, coupled with daily reports of dire news about the COVID-19 pandemic, could serve as catalysts for increased self-harm deaths (SHD). This ecological study examined whether observed SHD counts were higher than predicted SHD counts during the pandemic period in the Canadian provinces of Alberta, British Columbia, Ontario, and Quebec. The study also explored whether SHD counts during the pandemic were affected by lockdown severity (measured using the lockdown stringency index [LSI]) and COVID-19 case numbers. We utilized publicly available SHD data from January 2018 through November 2020, and employed AutoRegressive Integrated Moving Average (ARIMA) modelling, to predict SHD during the COVID-19 period (March 21 to November 28, 2020). We used Poisson and negative binomial regression to assess ecological associations between the LSI and COVID-19 case numbers, controlling for seasonality, and SHD counts during the COVID-19 period. On average, observed SHD counts were lower than predicted counts during this period (p < 0.05 [except Alberta]). Additionally, LSI and COVID-19 case numbers were not statistically significantly associated with SHD counts.

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