Early Detection and Assessment of Covid-19

Background: Since the Covid-19 global pandemic emerged, developing countries have been facing multiple challenges over its diagnosis. We aimed to establish a relationship between the signs and symptoms of COVID-19 for early detection and assessment to reduce the transmission rate of SARS-Cov-2. Methods: We collected published data on the clinical features of Covid-19 retrospectively and categorized them into physical and blood biomarkers. Common features were assigned scores by the Borg scoring method with slight modifications and were incorporated into a newly-developed Hashmi-Asif Covid-19 assessment Chart. Correlations between signs and symptoms with the development of Covid-19 was assessed by Pearson correlation and Spearman Correlation coefficient (rho). Linear regression analysis was employed to assess the highest correlating features. The frequency of signs and symptoms in developing Covid-19 was assessed through Chi-square test two tailed with Cramer's V strength. Changes in signs and symptoms were incorporated into a chart that consisted of four tiers representing disease stages. Results: Data from 10,172 Covid-19 laboratory confirmed cases showed a correlation with Fever in 43.9% (P = 0.000) cases, cough 54.08% and dry mucus 25.68% equally significant (P = 0.000), Hyperemic pharyngeal mucus membrane 17.92% (P = 0.005), leukopenia 28.11% (P = 0.000), lymphopenia 64.35% (P = 0.000), thrombopenia 35.49% (P = 0.000), elevated Alanine aminotransferase 50.02% (P = 0.000), and Aspartate aminotransferase 34.49% (P = 0.000). The chart exhibited a maximum scoring of 39. Normal tier scoring was ≤ 12/39, mild state scoring was 13–22/39, and star values scoring was ≥7/15; this latter category on the chart means Covid-19 is progressing and quarantine should be adopted. Moderate stage scored 23–33 and severe scored 34–39 in the chart. Conclusion: The Hashmi-Asif Covid-19 Chart is significant in assessing subclinical and clinical stages of Covid-19 to reduce the transmission rate.

Coronavirus 2 (SARS-Cov-2), caused the current global pandemic (2). During this pandemic, the most critical questions aroused pertains to patients and clinicians in understanding how the disease spread to cause an epidemic, what its clinical presentation with a severity profile is, what assessment or diagnostic measures should be used, and what projected treatments and influences to prognosis and recurrence there are.
Covid-19 has threatened the entire world. For the health services providers, it became a challenge to make rapid forward planning to evaluate the transmission rate of SARS-Cov-2 without ready access to diagnostic techniques and future planning based on the sustainability of healthcare systems to cope with the outbreak (3). Pragmatic understanding of the novel pathogen SARS-Cov-2 revealed an essential genetic sequencing similarity to the previously known pathogen, SARS (4). A mean incubation period of 5.2 days of SARS-Cov-2 has been reported to cause the onset of symptoms and a mean 12.5 days for hospitalization from day of infection (5,6). Fauci et al. emphasized the time interval during the incubation of SARS-Cov-2 to hold crucial information on pathogenesis and asserted the need to understand it to design an effective containment policy (7). Current understanding of Covid-19 pathogenesis focuses on the Angiotensin Converting Enzyme 2(ACE2) based SARS-Cov-2 cell entry that infects lung epithelial cells and synergistic entry through endosome proteases cell prime entry that infects the host cell (8). Novel coronavirus also infects T-lymphocytes (9). Recent retrospective studies revealed that elders are more prone to Covid-19 and were more likely to require invasive mechanical ventilation with a high mortality among Covid-19 infected patients, and robust research revealed the clinical presentation of Covid-19. Currently, Covid-19 is detectable with Reverse Transcriptase Polymerase Chain Reaction (RT-PCR), which detects presence of genetic fragments of SARS-Cov-2 within secretions from nasal and pharyngeal epithelial mucus membrane. Employed techniques of RT-PCR and immunoglobulin presence detection methods have their own limitations of detection within a specific time period. Prior to detection through RT-PCR, no method is available to assess Covid-19 infection during incubation and after the onset of symptoms. Consequently, a high transmission rate has been reported and needs to be reduced for effective containment (7). In this study, we evaluated the current knowledge of Covid-19 pathogenesis and its manifestation to formulate an easy method to detect and assess the Covid-19 course of infection and to counter outbreaks by reducing transmission rates through early sensing and adopting appropriate measures.
Comparatively similar clinical features were previously reported to be caused by influenza. Influenza, caused by H1N1, H3N2, and H5N1, produced variable symptoms in humans. Median incubation periods are 2 days, 1-6 days, and 2-5 days, respectively. All strains cause acute symptoms variable in nature and intensity (10)(11)(12)(13)(14)(15)(16). H1N1 causes a fever similar to H3N2, with a relatively shorter duration of 1-2 days while H3N2 causes a fever of 1-6 days. Avian influenza (H5N1) presents with baffling symptoms aggressive in nature, like inexplicable diarrhea or encephalopathy. Intensity of the symptoms is high and related with areas of known outbreaks. Fever (temperature > 38 • C) is present in symptomatic patients with abdominal features including vomiting, diarrhea, myalgia or arthralgia, rhinorrhea, cough, and sputum production. All signs and symptoms appeared concomitantly on median 2 or 3 days after infection (10). H1N1 causes symptoms to appear on day 2. The virus is detectable during a median period of 2-6 days after infection. Sore throat, nasal congestion, nausea, vomiting, and myalgia are common symptoms with a mild to severe fever. Distinguishing signs are enlarged lymph nodes, tonsillitis, and throat congestion while prominent features are leukopenia, lymphopenia, and hypokalemia (11,12). H3N2 significantly reduced the weight of patients during the early days of infection (13,14). Severe cases of H5N1 presents with cardiomyopathies, ventricular tachycardia, renal failure, ventilation assisted viral pneumonia, Reye's syndrome, and pneumothorax. Death occurred due to multi-organ failure. Blood biomarkers abruptly developed leukopenia, lymphopenia, thrombocytopenia, and elevated aminotransferases (15,16).
Clinical manifestations of SARS-Cov-2 appeared variable as compared to influenza. Symptoms of Covid-19 also vary slightly from region to region. Abdominal symptoms were more frequent in the USA than China (17)(18)(19)(20)(21). Asymptomatic, mild, and severe symptoms were observed in various studies (22)(23)(24)(25)(26)(27). Asymptomatic or milder cases did not seek medical intervention; mild symptoms included a temperature >37.5 • C and dry cough initially and could develop to moderate symptomatic cases. Fever, cough, abdominal discomfort, and deranged blood biomarkers were recorded in moderate cases. Severe cases presented with shortness of breath, dyspnea, and tachypnea and required mechanical ventilation (28). Persistent cough, fever, and fatigue were associated symptoms of an underlying pathology or pre-existing pathology not restricted to cardiovascular issues, hypertension, liver compromise, and diabetes. Blood pO 2 levels decreased. Blood biomarkers developed lymphopenia, thrombopenia, and elevated aminotransferases in moderate and severe cases. White blood cells deteriorated in severe cases and required mechanical ventilation. Persistent fever and characteristic consistent coughing-initially dry for several days followed by a productive cough-are the main features in patients with pre-existing respiratory infections; a few symptoms were variable with geographical regions (29)(30)(31)(32)(33)(34)(35)(36)(37)(38). In the current study we emphasized the pathogenesis of Covid-19 assessed through signs and symptoms and its manifestation to formulate a practicable approach to detect and assess Covid-19's course of infection to counter outbreaks by reducing the transmission rate through early sensing and adopting appropriate measures.

Data Collection
We used a retrospective approach to collect observational data about the most common presenting signs and symptoms in reported cases of Covid- 19

Interpretation of Data
Data was assessed for common presentations made by collected publications for sensing essential common symptoms. The Hashmi-Asif Covid-19 Formula was designed based on collected data that adhered to the most common and easily accessible symptoms which can affect an early diagnosis of Covid-19 or, due to their absence, could delay diagnosis or cause misdiagnosis. The important differentiating clinical features, signs, and symptoms were aligned in table form providing a sketch of the most common essential symptoms. Data about the frequency of symptoms in relation with Covid-19 diagnosis were categorized into clinical features and blood biomarkers. We categorized common symptoms and blood biomarkers for Covid-19 extracted from the collected data and these were categorized into two groups.

Classification of Data
The classification of normal to severe symptoms was determined from collected data containing values, ratios with interquartile ranges, and percentages of occurrence in observational studies. Four scoring tiers were formulated. Each sign and symptom were assigned a score by using the Borg Scale scoring method previously described by Hommerding et al. (39) with slight modifications. Signs and symptoms were given a score between 1 and 4. Normal signs and symptoms were given a score of 1 and placed in the first tier, mild presentation in signs and symptoms were given a score of 2 and placed in the second tier, third tier includes moderately presenting symptoms given a score of 3, and severe cases were given a score of 4 in the fourth tier. The highest score in the fourth tier scores 39 which represents severe disease while the lowest in the first tier scores 11 and showed normal or no disease. Mild disease scored between 13 and 22 and moderate disease scored between 23 and 33. Variable scoring showed stages of the disease as mild, moderate, or severe. Minimum and maximum scores were calculated and evaluated for the available data collected and compiled in Table 1. All data were calculated on the score chart to evaluate its efficacy for detecting early common signs and symptoms to make an easy decision on whether to hold isolation and other immediate measures surrounding the early confirmation of Covid-19. The chart was given the name of the Hashmi-Asif Covid-19 formula for calculating early common signs and symptoms of Covid-19 for early detection and disease assessment.

Statistics
We investigated the relationship of frequent appearances of common signs and symptoms with diagnosed Covid-19 cases by Pearson correlation and Spearman correlation coefficient (rho) two-tail (38). Cumulative frequencies of each common sign and symptom were assessed by Chi-square test two tail with Cramer's V strength methods (40). Highly significant symptoms and signs showing correlation were assessed by the linear regression method to establish ostensible correlation. Compiled data was analyzed statistically by using IBM SPSS Version.20.

RESULTS
Results of 10,172 confirmed Covid-19 cases showed the appearance of signs and symptoms in relation to the pathological progression of Covid-19. Infection leads to initial changes that occurred in blood biomarkers and, when reaching threshold level, produced symptoms (Details are shown in Table 1). All signs and symptoms cumulatively showed a 39.33% sensitivity correlation with the cumulative scoring method and a 48.11% through star values scoring method among all cases evaluated for Covid- 19. Data showed that if all the confirmed cases were analyzed before confirmation with the early signs and symptoms at 39.33 and 48.11% with star values, cases could be detected earlier than usual in the course of disease, and would be considered at very high risk of developing Covid-19.

Statistical Analysis
Twenty studies containing detailed information of 10,172 Covid-19 laboratory confirmed cases showed a common symptomatic correlation with Covid-19 were statistically significant (sig.<0.000) for each sign and symptom. Fever at 43.9% was significant 0.000. Cough at 54.08% and dry mucus membrane at 25.68% values were equally significant 0.000, hyperemic mucus membrane at 17.92% was significant with p < 0.005, leukopenia (28.11%) and lymphopenia (64.35%) showed a significance of 0.000. Thrombopenia (35.49%) showed a strong correlation (sig.0.000) with Covid-19 at significant p (<0.01). Amino transferases ALT and AST (50.02 and 34.49%, respectively) showed a strong correlation and were statistically significant (<0.001). Thereafter, symptoms holding high sensitivity correlations (star values) with the development of Covid-19 were extracted by linear regression model. Statistical data is shown in Table 2. Symptoms frequency appearance in Covid-19 was assessed by Chi-square method and results shown in Table 2. Fever and lymphopenia frequency showed a similar significance (P < 0.000). Cough showed a significance frequent appearance in Covid-19 (P > 0.02). Dry mucus membrane and thrombopenia showed a similar significance (P < 0.006). Hyperemic mucus membrane did not show a significant value (P < 0.062), while aminotransferases showed an equal significance (P < 0.001).

Hashmi-Asif Covid-19 Chart
Symptoms of Covid-19 were classified into early symptoms and late symptoms based on severity. Early symptoms can be a point of consideration for getting early detection. Covid-19 diagnosis could be missed during the early stage because of early symptoms being mild in nature. However, distinct evaluations for Covid-19 could be made by calculating scores of correlated blood biomarkers analysis through the Hashmi-Asif Covid-19 chart as elaborated in Chart 1. Common signs and symptoms were classified according to severity including normal with no disease, milder, moderate, and severe cases. The formula contains a maximum of 39 (15+24) scores, out of  long incubation period (6,37). A longer incubation period means certain opportunities to get prepared and prompt early action against Covid-19 to be opted. Asymptomatic cases may be diagnosed on the onset of the disease and earlier symptoms appearing during the course of the disease also holds a credible opportunity to make an earlier than usual diagnosis. Early detection could only be possible by assessing signs and symptoms evaluated from various studies.

CONCLUSION
We showed a strong correlation between the early and common signs and symptoms leading to the development of Covid-19 and designed the Hashmi-Asif Covid-19 chart which holds the potential of diagnosing 48.11% of asymptomatic Covid-19 cases earlier than usual. For symptomatic cases of Covid-19, the Hashmi-Asif Covid-19 chart holds a sensitivity of 95% to early detection, which will surely reduce transmission rate and prevent an epidemic outbreak or slow down its spread. The chart is also useful to assess the status of covid-19 in patients through regular scoring. The score decreases with amelioration of the Covid-19 situation. The chart can provide essential information about the efficacy of the management method being applied and whether it is useful or not, whether disease severity is reducing or not, and whether the bodily response is either ameliorating or worsening. This chart will help healthcare workers to implement timely measures for critical patients to save lives by opting for appropriate measures, and to make containment strategies to counter Covid-19.

Limitations
Our study has various limitations. It is a retrospective study based on reported clinical manifestations and probable courses of disease from available data around the world. Individual data of patients of Covid-19 were less reported and collective analyzed data was evaluated. A prospective study is underway to evaluate the utilization of the Hashmi-Asif Covid-19 assessment chart and its efficacy within domestic Covid-19 patients.

DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to the corresponding author.

ETHICS STATEMENT
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.