Exploring COVID-19 Progression Patterns

 

Zakarya¹, Al Zalak¹, Sahar Alfahoum², Razan Zohairee³

¹Ph.D, Damascus, Syria.

²Professor of Biochemistry, Damascus University, Syria.

³Department of Toxicology and Pharmacology, Master Degree, Damascus University, Damascus, Syria.

 

ABSTRACT:

Background: A novel coronavirus COVID-19 causing acute illness with severe symptoms, represents the causative agent of a contagious potentially lethal disease. COVID-19 was declared as pandemic by WHO. Aims: This Research aims to study the COVID-19 outbreaks in the fifteen most impacted countries in the world, find the relationship between the precautionary measures of governments and COVID-19 confirmed cases and deaths, and to forecast the pandemic in the following short time. Methods: The global numbers of confirmed cases and deaths of COVID-19 were obtained from the European Union Data. The data of governmentsʹ response actions for COVID-19 were estimated using the Oxford study. Box-Jenkins methodology, ARIMA model, R package were used in data analysis. Results: The rate of COVID-19 confirmed cases is 0.4 per thousand, and the death case rate is 0.03 per thousand of the world population. The rate of death cases was the lowest in Brazil, and the highest in Spain. The usefulness of precautionary measures and its effect on the number of confirmed cases and deaths in the different countries were estimated. A high correlation was established concerning the applied measurements and time of application. The model used for forecasting the expected cases was consistent with our tested result, while the model for forecasting death showed a fair consistently. Conclusion: We conclude that the health system must be reviewed, and these precautionary measures evaluated whether they are beneficial or more stringent conditions should be imposed.

 

 

 

INTRODUCTION:

Background: Emergence of new viral infections are major public health threats, in particular, viruses from wildlife hosts, causing diseases such as severe acute respiratory syndrome (SARS) in 2002 and Middle East Respiratory Syndrome (MERS) in 2012. A novel virus recognized as COVID-19 was discovered in Wuhan, China, at the end of 2019¹.

 

A group of patients was admitted to hospitals with an initial diagnosis of pneumonia of an unknown etiology on December 2019. These patients were epidemiologically linked to a wet and seafood animal wholesale market in the center of the Chinese city of Wuhan^2,3.

 

The first cases were reported on December 2019, some patients were hospitalized with acute respiratory distress syndrome SARS⁴. By January 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed COVID-19 infection, most of the patient were men 73%, and less than half of these patients had underlying diseases, including diabetes, hypertension, and cardiovascular disease⁵.

 

On 30 January 2020, World Health Organization WHO officially declared the COVID-19 epidemic a public health emergency of international concern, on 11 March, WHO announced the COVID-19 as a pandemic⁶. Scientists from several countries have analyzed genomes of the causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and they predominantly conclude that this coronavirus originated in wildlife^7,8. Previous studies have shown that some bat SAR Sr-CoVs have the potential to infect humans^9,10,11.

Coronavirus disease COVID-19 is caused by 2019-nCoV, the etiology of this illness is now attributed to a novel virus belonging to the coronavirus CoV family, and represents the causative agent of a potentially lethal disease. Two other coronavirus infections-SARS and MERS in both caused severe respiratory syndrome in humans¹², all three of these emerging infectious diseases leading to a global spread are caused by β-coronaviruses¹³. Common symptoms at onset of illness were fever, cough, and myalgia or fatigue; less common symptoms were sputum production, headache, haemoptysis, and diarrhea. Dyspnoea developed of some patients to acute respiratory distress syndrome and, acute cardiac injury⁵. The risk of serious illness and death in COVID-19 cases among patients increases with older age groups^14,15, and people of all ages with underlying medical conditions such as: Heart disease, diabetes, lung disease, liver disease, kidney disease, severe obesity and people who are immune compromised¹⁶. The risk of transmission from an individual with coronavirus 2 (2019- n CoV) infection varies by the type and duration of exposure, use of preventive measures, and likely individual factors (eg, the amount of virus in respiratory secretions). People who are infected often have manifest symptoms of illness. Some people who are asymptomatic may be able to affect our ability to contain and trace the spread of the virus¹⁷.  Most secondary infections have been described among household contacts, in congregate or health care settings when personal protective equipment was not used including hospitals and long-term care facilities¹⁸.

 

WHO recommended measures to slow COVID-19 progression, transmission, and prevent outbreaks. These measures include personal, environmental, and travel related interventions¹⁹. Governments have worked to deal with the virus by taking a set of measures; the University of Oxford has calibrated these measures. Oxford University has established calibration measures, which provided a systematic way to track the measurement adopted by each country facing COVID-19, and to describe variation in governments' responses²⁰.

 

We are going to describe the COVID-19 confirmed cases and deaths patterns in time series, and the relationship between the precautionary measures of each government to COVID-19 confirmed cases and deaths in selected countries, and forth to forecast the pandemic progression in the next two month following the preparing the manuscript.

 

Subject and Methods:

Data collection: The global numbers of confirmed cases and deaths of COVID-19 Were obtained from the European Union Data (EUD) from 31 Dec 2019 to 30 Apr 2020²¹.

The data of governmentsʹ response actions of COVID-19. Were estimated using the Oxford study. This data describes Oxford COVID-19 Government Tracker (OxCGRT) collection and presents some ‎basic measures of variation across governments²⁰.

 

Our study will consider COVID-19 confirmed cases and deaths in the fifteen most impacted countries in the world; The United States of America, Spain, Italy, Germany, the United Kingdom, France, Turkey, China, Iran, Russia, Brazil, Belgium, Canada, the Netherlands, and Switzerland.

 

Statistic methods and Models:

Firstly, we describe the total number and the rates of the COVID-19 confirmed cases and deaths patterns in the fifteen most impacted countries in the world each according to the total population of that country from 31 Dec 2019 to 30 April 2020.

 

Secondly, we describe the COVID-19 confirmed cases and deaths patterns over time by using the linear regression model in the selected countries. Thirdly, we compare the confirmed cases and deaths of COVID-19 with the governmentsʹ response to the measures adopted. Fourthly, we forecast the pandemic progression in May and June 2020. The Time Series Forecasting depending on The Box-Jenkins approach is applied^22,23.

 

‎‎"ARIMA", short for 'Auto Regressive Integrated Moving Average' is a class of models, that equation can be used in our study to forecast the future values‎ and check the residuals^24,25 by the R package which uses a variation of the Hyndman-Khandaker algorithm²⁶.

 

Results (Data Analysis):

We have obtained the following results.

 

First: Patterns description of COVID-19 confirmed cases and deaths worldwide.

The total number of confirmed cases in the world from 31 Dec 2019 until 30 April 2020 reached 3131302 cases, according to population worldwide, with cases rate 0.402 per thousand, and the total number of deaths reached 227319 cases with death rate 0.029 per thousand.

 

Since 31 Dec 2019, it can be described that the curve of the total number of COVID-19 confirmed cases took an upward trend worldwide, and the world reached the stabilization stage of infections during April 2020, the number of confirmed cases pattern peaked on 26 April 2020, at 101716 cases, ( Figure 1-A).

 

Fig. 1: confirmed cases, deaths patterns, worldwide, from 31 Dec 2019 until 30 Apr 2020

 

The peak of mortality exceeds the peak of confirmed cases in April 2020. For more details for every selected countries, see Appendix 1.

 

The total number of confirmed deaths pattern worldwide shows an escalating pattern, (Figure 1 –B), the highest mortality rate in the world was on 16 April 2020, where the total deaths reached 10520 cases, and our study shows a death rate of 1863 cases per day during the four shown months. The infections rate per day showed a clearly escalating pattern in 2020 (328 cases in Jan 2701 cases in Feb, 23066 cases in March and 78471 cases in Apr), with the rate for four months per day was 25666 cases. The mortality rate per day was 6314 cases in April.

 

The total number of confirmed cases pattern in the fifteen most impacted countries with the total number of mortalities in these countries until the date of preparing this paper was established (Table 1); it appears clearly that America, Spain, Italy, United Kingdom and Germany are leading in the number of confirmed cases respectively. America maintains the first rank in the number of mortality also followed by Italy, Spain, France, and the United Kingdom.

 

Table 1: confirmed cases and deaths rates, according to the population in the selected countries, from 31 Dec 2020 to30 Apr 2020

Order	Countries	Cases	Deaths	Population 2020 (thousands)	Cases Rate	Deaths Rate
1	Belgium	47859	7501	37742	1.268	0.199
2	Brazil	78162	5466	1439324	0.054	0.004
3	Canada	51587	2996	11590	4.451	0.258
4	China	83944	4637	84339	0.995	0.055
5	France	128442	24087	145934	0.880	0.165
6	Germany	159119	6288	67886	2.344	0.093
7	Iran	93657	5957	212559	0.441	0.028
8	Italy	203591	27682	60462	3.367	0.458
9	Netherlands	38802	4711	17135	2.264	0.275
10	Russia	99399	972	83993	1.183	0.012
11	Spain	216295	24543	46755	4.626	0.525
12	Switzerland	29324	1407	8655	3.388	0.163
13	Turkey	117589	3081	65274	1.801	0.047
14	United Kingdom	165221	26097	83784	1.972	0.311
15	United States of America	1039909	60966	331003	3.142	0.184
Total 15 top countries		2552900	206391	2696435	0.947	0.077
Total cases worldwide		3131302	227319	7794799	0.402	0.029

 

The rate of confirmed cases, according to the population in the selected countries, was established. Spain acquired the highest rate of infections and deaths, while Brazil has the lowest rate of confirmed cases and deaths. The rate of confirmed cases worldwide reached 0.4 per thousand compared to 0.03 of mortality per thousand, noting that the total number of confirmed cases in the selected countries represents 80% of the total confirmed cases in the world, while the total number confirmed deaths reached 90% of mortalities of COVID-19 in the world. The pattern of confirmed cases and deaths by country in Appendix 1. By using data according to continents, the rates of confirmed cases were distributed as not exceeding 1.3% in Africa, whereas the highest was in America reaching 43%, followed by Europe 40%, and Asia was 16%.

 

Second: The linear regression model of COVID-19 confirmed cases and deaths.

Where the linear regression in the selected countries of confirmed cases appears strongly over time in the USA and Canada followed by UK, Netherland and Turkey, respectively.

 

The linear regression model is weak with R= 0.22 in China as shown in (Table 2) and it appears that the number of confirmed cases tends to decrease significantly.

The linear regression model for these countries clarifies that the highest value with R= 0.83 was in Canada and USA, (Table 2).

 

By comparing the models of mortality among the selected countries, we found that the highest value of R regression was in Turkey, Italy, followed the UK and then the Netherlands, Iran and Canada, respectively and last USA and Germany, whereas the lowest significant value of linear regression model was in China R= 0.03. For more details, see Appendix 2 and 3.

 

Third: Precaution measurement in the selected countries and their relationship to COVID-19 confirmed cases and deaths.

The appearance of the confirmed cases according, to the onset in each country were in China, the USA, Canada, Germany, France and the UK, respectively by end of January and in Russia, Spain, Belgium, Iran, Italy, Brazil, Switzerland and Netherlands in February.

 

We depended on the Oxford study, to obtain the assignment index for our selected countries, and compared this index among each of the fifteen countries, by using a Spearman correlation coefficient, (Table 3).

 

Table 2: The linear regression model

Countries	R		R Square		Adjusted R Square		Significance
By	Cases	Death	Cases	Death	Cases	Death	Cases	death
USA	0.83	0.73	0.67	0.53	0.67	0.53	0.000	0.000
Spain	0.64	0.71	0.41	0.50	0.41	0.50	0.000	0.000
Italy	0.73	0.76	0.53	0.57	0.53	0.57	0.000	0.000
Germany	0.63	0.73	0.40	0.53	0.40	0.53	0.000	0.000
UK	0.82	0.76	0.67	0.58	0.67	0.57	0.000	0.000
France	0.63	0.63	0.40	0.39	0.39	0.39	0.000	0.000
Turkey	0.76	0.79	0.58	0.62	0.57	0.62	0.000	0.000
China	0.22	0.03	0.05	0.00	0.04	-0.01	0.004	0.000
Iran	0.70	0.74	0.49	0.55	0.49	0.54	0.000	0.000
Russia	0.69	0.66	0.47	0.43	0.47	0.43	0.000	0.000
Brazil	0.72	0.69	0.51	0.48	0.51	0.47	0.000	0.000
Belgium	0.74	0.69	0.54	0.48	0.54	0.47	0.000	0.000
Canada	0.83	0.74	0.68	0.54	0.68	0.54	0.000	0.000
Netherlands	0.77	0.76	0.58	0.57	0.58	0.57	0.000	0.000
Switzerland	0.54	0.66	0.29	0.43	0.28	0.43	0.000	0.000

 

Table 3: spearman correlation coefficients between precautionary measures and the selected countries

By	Index Brazil	Index Canada	Index Switzerland	Index China	Index Germany	Index France	Index United kingdom
Index Belgium	.918**	.903**	.916**	.252**	.944**	.955**	.939**
Index Brazil	1	.867**	.978**	0.072	.904**	.910**	.872**
Index Canada		1	.858**	.283**	.936**	.947**	.923**
Index Switzerland			1	.195*	.918**	.912**	.845**
Index China				1	.391**	.317**	.241**
Index Germany					1	.986**	.944**
Index France						1	.957**
Index United kingdom							1
Index Iran
Index Itely
Index Netherlands
Index Russia
Index Spain
Index Turkey
Index United States
N	113	108	107	113	108	113	113

 

 

 

 

Table No. 3: Continue

By	Index Iran	Index Itely	Index Netherlands	Index Russia	Index Spain	Index Turkey	Index United States
Index Belgium	.922**	.965**	.868**	.957**	.954**	.970**	.979**
Index Brazil	.928**	.916**	.958**	.913**	.897**	.894**	.930**
Index Canada	.858**	.943**	.840**	.939**	.929**	.951**	.916**
Index Switzerland	.949**	.909**	.935**	.895**	.886**	.876**	.918**
Index China	.235*	.337**	0.15	.246**	.288**	.255**	.266**
Index Germany	.914**	.977**	.847**	.966**	.951**	.960**	.960**
Index France	.905**	.984**	.843**	.975**	.958**	.970**	.971**
Index United kingdom	.858**	.958**	.796**	.971**	.962**	.963**	.961**
Index Iran	1	.931**	.899**	.900**	.888**	.897**	.917**
Index Itely		1	.848**	.974**	.967**	.974**	.978**
Index Netherlands			1	.872**	.852**	.840**	.865**
Index Russia				1	.977**	.975**	.973**
Index Spain					1	.961**	.970**
Index Turkey						1	.967**
Index United States							1
N	111	113	100	113	113	113	113

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

 

The Spearman correlation coefficient was significant among all countries, with the exception of China where the relationship appears weak with all the selected countries. The relationship between the number of confirmed cases and deaths in the selected countries with the index was established, it was very high, (Table 3). For more details, see Appendix 4 and 5. The lowest value of the correlation was in China, with 0.541 for confirmed cases and 0.68 for deaths, the value of both was significant, (Table 4).

 

Table 4: Correlation Spearman between confirmed cases and deaths, according to index.

Spearman's rho	Deaths	Cases
Index Belgium	.814**	.898**
Index Brazil	.878**	.942**
Index Canada	.833**	.877**
Index Switzerland	.884**	.956**
Index China	.686**	.541**
Index Germany	.815**	.920**
Index France	.903**	.900**
Index UK	.855**	.880**
Index Iran	.946**	.956**
Index Italy	.902**	.930**
Index Netherlands	.938**	.943**
Index Russia	.744**	.861**
Index Spain	.867**	.895**
Index Turkey	.814**	.851**
Index USA	.896**	.905**

** Correlation is significant at the 0.01 level (2-tailed).

 

Fourth: Forecasting COVID-19 confirmed cases and deaths using ARIMA models.

ARIMA models provide an approach to time series forecasting. That aims to describe the autocorrelations in our data of the number COVID-19 confirmed cases and deaths, first for the whole world. The second for each of the selected countries.

Through using automated algorithm at the world level, Figure 2 shows the number of confirmed cases with 80% and 95% probability. The forecasted average number of confirmed cases during May and Jun 2020 [6.8-12.2] million cases with 80% probability, and [5.4-13.5] million confirmed cases with 95% probability. According to ARIMA (0,1,1) model with drift. Moreover, the point forecast in average 95075 cases per day [68337-121812] with 80% probability, and [54183-135967] with 95% probability.

 

 

Fig 2: the best model of Forecast for the number of confirmed cases worldwide, *with drift

 

The next step was to check the residuals by plotting the autocorrelation function (ACF) of the residuals, and doing a portmanteau test of the residuals. Figure 3 shows the ACF plot of the residuals from the ARIMA (0,1,1) with drift model showing that all autocorrelations approximately are within the threshold limits, indicating that the residuals are behaving like white noise.

 

Fig. 3: residual plots for the ARIMA (0,1,1)with drift model for the number of confirmed cases worldwide.

 

By the same steps, we generated the model time series forecasting to the number of death cases in the world (Figure 4), the best model ARIMA(1,1,1) with the drift. Through that, we established the number of deaths with 80% and 95% probability. The average number of deaths during May and Jun 2020, was [319653- 637726] respectively with 80% probability and [235464- 721915] deaths with 95% probability. In addition, the point forecast in average was 7978 deaths per day [5328-10629] with 80% probability, and [3924-12032] with 95% probability.

 

Fig. 4: process to generate the best model for the number of deaths worldwide

 

Secondly, we summarized all process to generate models for the number of confirmed cases and deaths for each of the selected countries; Table 5 shows the best models of forecast for confirmed cases and deaths with patterns for each country in Appendix 6 and 7.

 

Table 5: forecast models of COVID-19 confirmed cases and deaths with patterns by country

Countries		Model	ACIC	Ljung-Box Test		Patterns	Forecast power
				Q	P- Value		Unit root test
Belguim	Cases	ARIMA(2,1,2)	1672	7.18	0.305	Dedcrease	Good
	Deaths	ARIMA(2,1,1)	1245	22,39	0.002	Stable	Good
Brazil	Cases	ARIMA(0,2,2)	1769	35	0.000	Increase	Good
	Deaths	ARIMA(3,1,0) *	1192	5.86	0.439	Increase	Fair
Canada	Cases	ARIMA(0,1,1) *	1580	12.28	0.139	Increase	Good
	Deaths	ARIMA(1,1,0) *	972	44.11	0.000	Increase	Good
China	Cases	ARIMA(0,1,1)	2084	2.06	0.990	Stable	Good
	Deaths	ARIMA(0,0,0)**	1522	2.09	0.990	Stable	NO
France	Cases	ARIMA(3,1,0)	1949	7.81	0.350	Stable	Good
	Deaths	ARIMA(0,1,1)	1629	24.87	0.003	Stable	Good
Germany	Cases	ARIMA(3,1,3)	1903	31.41	0.000	Unstable	Bad
	Deaths	ARIMA(2,1,2)	1114	17.87	0.007	Unstable	Bad
Iran	Cases	ARIMA(0.1.2)	1828	1.29	0.996	Stable	Good
	Deaths	ARIMA(1,1,2)	1120	2.68	0.913	Decrease	Fair
Italy	Cases	ARIMA(0,1,0)	1811	56.43	0.000	Stable	NO
	Deaths	ARIMA(2,1,2)	1320	13.99	0.030	Unstable	Bad
Netherlands	Cases	ARIMA(2,1,2)	1430	15.2	0.019	Unstable	Bad
	Deaths	ARIMA(0,1,1)	1114	39.41	0.000	Stable	Good
Russia	Cases	ARIMA(2,2,1)	1683	23.92	0.001	Increase	Good
	Deaths	ARIMA(2,2,2)	700	28.14	0.000	Increase	Good
Spain	Cases	ARIMA(2,1,2)	1960	15.53	0.017	Stable	Fair
	Deaths	ARIMA(1,1,3)	1405	9.56	0.145	Increase	Fair
Uk	Cases	ARIMA(5,1,4) *	1854	18.41	0.000	Stable	Fair
	Deaths	ARIMA(2,1,0)	1506	47.4	0.000	Unstable	Good
USA	Cases	ARIMA(0,1,1) *	2331	23.42	0.003	Increase	Good
	Deaths	ARIMA(5,1,1)	1779	13.8	0.008	Unstable	Fair
Turkey	Cases	ARIMA(0,1,0)	1709	51.05	0.000	Stable	NO
	Deaths	ARIMA(1,1,1)	653	17.35	0.027	Decrease	Good
Switzerland	Cases	ARIMA(1,1,0)	1539	55.49	0.000	Stable	Good
	Deaths	ARIMA(2,1,2)	934	12.88	0.045	Decrease	Good
Total	Cases	ARIMA(0,1,1) *	2437	22.96	0.003	increase	Good
	Deaths	ARIMA(1,1,1) *	1934	59.1	0.000	increase	Fair

*With drift, **With non -zero mean

 

DISCUSSION:

COVID-19 formed a state of emergency in the world, after the announcement by the WHO,²⁷ the governments scrambled to limit the transmission of COVID-19 because of the increasing number of confirmed cases and deaths, and it constituted an unprecedented challenge.

 

Upon returning to the rate of morbidity and mortality at the level of the world, we found that the rate of confirmed cases is 0.4 per thousand, and the death cases rate is 0.03 per thousand.

 

The mortality rate due to COVID-19 is small figures compared to other types of deaths, such as mortality of cancer²⁸ and cardiovascular diseases²⁹.

 

The rate of death cases was the lowest in Brazil with 0.004 per thousand, and the highest in Spain with 0.525 per thousand coincidence with the highest case of confirmed cases 4.626 per thousand. The forecast models show an expected increase in the number of confirmed cases in Brazil in the next two months (Table 5).

 

We see when the occurrence of COVID-19 confirmed cases and deaths coincided at the same time, despite lower rates that leads to the epidemic explosion, which made the health system impotent, so the health system must be leading to the need re- reconstructed of the health system. The results of linear regression model for COVID-19 confirmed cases show that China has a weak correlation over time, and therefore it can be said that China has overcome the crisis as the correlation significant value is 0.2, Switzerland follows it as the correlation coefficient value is approximately 0.5, and then Germany and France, and Spain. While the number of confirmed cases in both the United States of America and Russia was increasing, as the value of the correlation coefficient is greater than 0.8.

 

As for mortality, we found that the correlation was significant for all the selected countries and rather close, except for China, which turned out to have already passed the crisis (R Squire= zero). The correlation significance also shows that France and Switzerland followed China (R Square = 0.39, 0.43 respectively). Which are heading towards exceeding the crisis by the value of the correlation significant, with a downward trend whereas the correlation for mortality in Russia although the same value, shows an upward trend in the escalating stages, and so is the case for the United States of America (R Square =0.53). The result of comparing the index among countries for precautionary measures shows that the correlation coefficients is very strong, and this indicates that the strength of the procedures did not differ greatly from one country to another. The result may be that China has weathered the crisis, and the results of the precautionary measures have clearly begun to show. This means the impact of the measures taken has not yet shown, its impact on reducing the confirmed cases rate, and confirmed deaths after. As for comparing index for precautionary measures with the number of confirmed cases and deaths for each country, we find that the relationship is still strong. The reason may be due to the delay in taking precautions, or not complying with the measure. These findings are in agreement with the Chinese study (Yichi et al)³⁰. they believe that the emergency intervention measures adopted in the early stage of the epidemic, had a crucial restraining effect on the original spread of the epidemic. With respect to the forecast model worldwide, we find that the forecast confirmed cases model is a good model, while the forecast model that has been adopted for deaths cases worldwide is a fair model. By reviewing the number of recorded confirmed cases worldwide, we found this number to be within the 80% probability zone, while it was within the region 95% for deaths. The same applies to models of confirmed cases at the level of each country, most of which were within the forecast limits, except for models that were not valid, as in Italy and Turkey, as the models in those countries did not meet the requirement of invertibility and therefore the models are rejected.

 

The same applies to China with respect to the model of deaths, which may be the reason for that recognition of a number of recent deaths of 1290 cases, on 17 Apr 2020.

 

CONCLUSION:

We conclude that the health system must be reviewed, and these precautionary measures evaluated whether they are beneficial or more stringent conditions should be imposed.

 

LIMITATION:

The article assumed that the confirmed cases represent all cases in the country, whereas it is relevant to the number test performed.

 

The data should include the number of tests performed per day in each country.

 

ACKNOWLEDGEMENT:

Prof. Yousser Mohammad, Professor of Chest Diseases at Faculty of Medicine at Tishreen University in Syria.

 

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Accepted on 28.10.2021           © RJPT All right reserved

Research J. Pharm.and Tech 2022; 15(3):1299-1306.