How does the 2019 Novel Coronavirus (nCoV2019) compare to Ebola, SARS, MERS? Cases and Deaths
Weighing diseases against each other is complicated but it puts things into perspective. nCoV2019 has surpassed SARS in terms of known infections but case-fatality remains stable (~2%). Too early to tell how far nCoV might travel.
Ebola virus (Identified: 1976) - DR Congo, Uganda: (2018 - ongoing) - 34,453 Confirmed cases, 15,158 deaths
SARS coronavirus (November 2002 - July 2003) - 8,437 Confirmed cases, 813 deaths
MERS coronavirus (2012 — ongoing) - 2,506 Confirmed cases, 862 deaths
2019-nCoV (December 2019 — ongoing) - 37,595 Confirmed cases, 2,933 recovered, 814 deaths
As of 09 February 2020
Dr. Melvin Sanicas @Vaccinologist
#nCoV2019 #Novel #Coronavirus #comparison #viruses #MERS #Ebola #SARS #virology #mortality #deaths #cases #COVID19
USMLE Epidemiology and Biostatistics Summary
Meta-Analysis: pools data from several studies (greater power), limited by quality/bias of individual studies
Clinical Trial: compares two groups in which one variable is manipulated and its effects measured
Cohort (relative risk): compares group with risk factor to a group without – asks “what will happen?” (prospective). Proves
cause-effect
Case Control (odds ratio): compares group with disease to group without disease – asks “what happened?” (retrospective).
Issues with confounding and inability to prove causation
Case Series: good for rare diseases, describe clinical presentation of certain disease
Cross-Sectional: data from a group to assess disease prevalence at a particular point in time – asks “what is happening?”
Sensitivity (rule out – screening): proportion of people with
disease who test positive: TP / (TP + FN) = 1 - FN. If 100%,
then all negative tests are TN.
Specificity (rule in – confirmatory): proportion of people
without disease who test negative: TN / (TN + FP) = 1 - FP.
If 100%, then all positive tests are TP.
PPV: proportion of positive tests that are true positives: TP / (TP + FP). If disease
prevalence is low, then PPV will be low.
NPV: proportion of negative tests that are true negatives. TN / (TN + FN)
Higher specificity -> higher PPV Higher sensitivity -> higher NPV
Odds ratio (case control): odds of having disease in exposed group divided by odds in
unexposed group. (a/b) / (c/d) = (ad) / (bc)
Relative risk (cohort): relative probability of getting disease in exposed group versus
unexposed. [a/(a+b)] / [c/(c+d)]
Attributable risk: proportion of cases attributable to one risk factor.
[a/(a+b)] - [c/(c+d)]
Absolute risk reduction (ARR): [c/(c+d)] - [a/(a+b)]
NNT = 1 / ARR
Standardized mortality ratio (SMR) = observed No deaths / expected No deaths
Incidence: No of new cases in a unit of time/ pop. at risk
Prevalence: total No of cases at a given time / pop. at risk
Prevalence = incidence * dz duration. Prevalence > incidence in chronic dz. Prevalence = incidence in acute dz
Normal distribution: mean = median = mode
Standard deviation: 1 (68%) – 2 (95%) – 3 (99.7%)
SEM = σ / √n
Positive skew (mean > median > mode), negative skew (mean < median < mode)
Reliability (“precision”) – reproducibility of test. Affected by random error
Validity (“accuracy”) – measures trueness of data. Affected by systematic error
Correlation coefficient measures how related two values are:
+1 = perfect positive correlation, -1 = perfect negative correlation, 0 = no correlation
H0 (null hypothesis): no relationship between two measurements
Type I (α) error: reject null when it’s true
Type II (β) error: accept null when it’s false
Power (1-β): probability of rejecting null when it is indeed false (increase sample size to increase power)
Selection bias: nonrandom assignment of subjects
Sampling bias: subjects not representative of population
Recall bias: risk for retrospective studies (pts cannot remember things); knowledge of disorder presence alters recall
Late-look bias: data gathered at inappropriate time
Lead-time bias: early detection confused with increased survival
Confounding bias: a factor is related to both exposure and outcome, but not on the causal pathway
Procedure bias: subjects in different groups not treated the same
- Rishi Kumar, MD @rishimd
#USMLE #Epidemiology #Biostatistics #formulas #calculations #diagnosis
