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Novel Use of Capture-Recapture Methods to Estimate Completeness of Contact Tracing during an Ebola Outbreak, Democratic Republic of the Congo, 2018–2020 – The Maravi Post

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Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released.

Author affiliations: World Health Organization, Geneva, Switzerland (J.A. Polonsky); University of Geneva, Geneva (J.A. Polonsky, M. Keita, J. Estill, O. Keiser, L. Kaiser); University of Southampton, Southampton, UK (D. Böhning); World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo (M. Keita, Z. Yoti); Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo (S. Ahuka-Mundeke, M. Mossoko); Ministere de la Santé Publique, Kinshasa (J. Nsio-Mbeta, A. Aruna Abedi); Thammasat University, Bangkok, Thailand (P. Sangnawakij, R. Lerdsuwansri); World Health Organization South East Asia Regional Office, New Delhi, India (V.J. Del Rio Vilas)

Contact tracing is the process by which persons who are believed to have come into contact with a person with a confirmed case of an infectious disease during their infectious period are located and checked for the presence of the infection or disease. Under traditional approaches, contact tracing involves 3 distinct steps: contact identification, in which potential contacts are identified through interview with the primary case-patient; contact listing, in which those identified contacts are listed and communication established with them; and contact follow-up, in which those listed contacts are monitored for presence of infection or onset of disease over a predefined period (1).

Because of its important role in case detection to monitor and curtail chains of transmission, contact tracing often forms part of the public health response to directly transmitted infectious diseases (2). Recently, contact tracing has received widespread attention because of its critical role in the response to outbreaks of diphtheria (3), Ebola virus disease (EVD) (46), and the ongoing coronavirus disease (COVID-19) pandemic (7,8).

During 2018–2020, the Democratic Republic of the Congo (DRC) experienced its 10th and largest EVD outbreak, the second largest ever experienced globally (9). EVD is a disease caused by viruses of the genus Ebolavirus, family Filoviridae. Zoonotic spillover events from the animal reservoir have led to large, explosive outbreaks in West and Central Africa in recent years (912). Owing to the high pathogenicity and virulence of Ebola virus, an elimination control strategy is always adopted, aiming to ensure that all case-patients are identified, isolated, and treated promptly after disease onset, thereby limiting the opportunity for onward community spread. Although contact tracing is a central pillar of control (13), no standardized methods have been established to assess a critical aspect of performance, its sensitivity (i.e., the ability to detect all contacts and secondary infections resulting from case-patients).

One approach to quantifying this metric is to employ capture-recapture (CRC) methods (14,15). Broadly, this family of methodologic approaches enables researchers to quantify any unit of interest missing from lists and subsequently estimate the sensitivity of the surveillance effort and the probability of detection. Although CRC has previously been used to estimate the number of unobserved cases of disease (16,17), such approaches typically rely on comparison of multiple lists, which are generally not available for contact lists. Therefore, we describe the application of a unilist capture-recapture approach (15) to quantifying the number of unobserved case-patients and contacts and describe their sociodemographic profile, helping to identify plausible risk factors that can be used to target limited resources at those unobserved case-patients most likely to generate onward transmission. More precisely, we aimed to address 2 questions, from which we can derive contact tracing sensitivity estimates: how many case-patients with any contacts did contact tracing miss, and how many case-patients with infected contacts did contact tracing miss?

Materials and Methods

Study Participants

We included all confirmed and probable EVD case-patients and contacts (classified according to standardized case definitions [18,19]) identified in Beni Health Zone, DRC, during July 31, 2018–April 26, 2020. Case-patients were principally detected through 3 identification mechanisms: passive detection at healthcare facilities from persons manifesting symptoms consistent with EVD, house-to-house active case-finding by community health workers, and tracing the contacts of EVD case-patients. Contact tracing was coordinated by the DRC Ministry of Public Health, with support from the World Health Organization, and conducted by locally recruited teams of contact-tracers. Upon detection of a case, efforts to identify and list the case-patient’s contacts were undertaken.

For case-patients, our data contain basic information on sociodemographic characteristics (e.g., age, sex, and DRC Health Area of residence) and dates of disease onset and isolation. For contacts, our data contain similar sociodemographic information and information on the daily follow-up and final status of the contact (either “completed the 21 days follow-up,” “confirmed as EVD case-patient,” “lost to follow-up,” “never seen,” or “died during follow-up”). Contacts recorded as “confirmed as EVD case-patients” were those identified by the contact tracing teams during the course of their work. EVD was assumed to be the cause of death for contacts recorded as “died during follow-up” because of the short interval between their contact with an EVD case-patient and their death.

Exploratory Data Analysis

We determined the distribution of case-patients according to age, sex, and timing of disease onset. We used the Wilcoxon test to explore differences in continuous variables and the χ2 test for categoric variables to determine the distribution of the number of contacts per case-patient between 2 distinct epidemic waves. Overdispersion (i.e., superspreading) in the offspring distribution of secondary case-patients arising from infectious persons may have profound effects on control strategies in low-resource settings (20,21), and we describe the extent of this phenomenon in 2 ways: first, by assessing the proportion of infectious persons linked to 80% of onward transmission using methods described by Endo et al. (22); and second, by estimating the dispersion parameter (k) using methods described by Althaus (23).

We used a multivariable logistic regression model to explore risk factors associated with loss to follow-up, in which previously successfully traced contacts (i.e., those identified, listed, and among whom follow-up has begun) become untraceable at some point during the 21-day follow-up period. In such instances, contacts unable to be traced for 3 consecutive days are recorded as having been lost to follow-up, and no further attempts at tracing are made. To explore characteristics of case-patients with infected contacts, we calculated the mean number of contacts, mean age, and sex ratio of case-patients with >1 listed contact (among whom we can be confident that at least a minimal investigation was conducted), according to 3 categories: those with no infected contacts identified, those with exactly 1 contact, and those with >2 contacts.

CRC Modeling

We classified the observed case-patients according to their number of listed contacts (either exactly 0 or >1 contact), further classifying this latter category according to the number of infected contacts observed (either exactly 0 or >1 contact). For each detected case, the contact tracing process generates a list of persons fitting the definition for a contact (Appendix), some of whom may themselves have been infected and will eventually become secondary case-patients. From this list, frequency distributions of case-patients with any listed contacts, and of case-patients with infected contacts, can be generated by first excluding (truncating) those case-patients with 0 contacts. For example, the data can be binned into the number of case-patients with exactly 1 contact (f1), 2 contacts (f2), and so on, to the number of case-patients with the maximum number of contacts (fm). Statistically, this process leads to a 0-truncated observed count distribution of case-patients with >1 contact. By applying a unilist CRC approach designed to estimate unobserved population sizes using the distribution of count data within single lists (15), we can infer f0, the number of unobserved case-patients with >1 contact. Associated with the observed frequencies (f1, f2,…, fm) and unobserved f0 are probabilities p1, p2,…, pm and p0 that inform the probability of identifying a case-patient with exactly 1, 2,…, m and 0 contacts, respectively. A conventional approach assumes that the frequencies arise from a discrete distribution such as the Poisson, where

Other common distributions are the negative binomial and the geometric distribution. The geometric distribution has probabilities p0 = p, p1 = p(1 – p), p(1 – p)2pm = p(1 – p)m, where p is a probability parameter. Poisson and geometric are special cases of the negative binomial distribution, which provides a flexible model family (Appendix). Because the observed distribution contains only positive numbers of contacts, we need to consider the associated zero-truncated distribution p1/(1 – p0), p2/(1 – p0),…pm/(1 – p0) In other words, we assume that the number of observed contacts among case-patients who actually had contacts follows a parametric distribution (although nonparametric approaches are possible [15,24,25]), find the best-fitting zero-truncated distribution of case-patients with >1 observed contact (we explore the zero-truncated Poisson, negative binomial, and geometric distributions [Appendix]), and use the estimated probability p0of not observing a case-patient with contacts (calculated from the best-fitting distribution) to inform standard population estimators. We use the Horvitz–Thompson estimator to estimate f0, the unobserved number of case-patients:

where n is the number of observed case-patients with >1 observed contact and p0is as previously defined. The Horvitz–Thompson estimator provides an unbiased estimate of f0, provided that p0 is correctly specified; hence, using a correctly-specified distribution for the number of observed contacts is important. We use maximum likelihood for model fitting, selecting the model with the smallest Akaike information criteria (AIC) and Bayesian information criteria (BIC) (Appendix).

To estimate 95% CIs, we use a parametric bootstrap, described as follows. Suppose that is the estimated size of the (observed and unobserved) population under a fitted model. We generate B samples of size using the fitted model and its estimated parameter or parameters. For each sample, all zeros are truncated and the size estimate b computed, for each of the samples b = 1, …, B. We chose B = 10,000 to minimize bootstrap simulation random error. We constructed 95% CIs by using the 2.5th percentile of the distribution of b as the lower end and the 97.5th percentile as the upper end.

Results

Exploratory Data Analysis

We identified 913 confirmed and 10 probable EVD case-patients in Beni Health Zone. The contact tracing process listed 80,556 contacts, of whom 6,224 were duplicates, having been listed as the contact of >1 case-patient, resulting in 74,181 contacts to trace. In discussion with contact tracing teams, duplicates were identified by matching name and residential location; for operational reasons, these persons were recorded as a contact of only the earliest-identified primary case-patient with whom they were associated. More than half of case-patients for whom sex and age information were available were women and girls (n = 515 [55.8%]); median age for all case-patients was 25 years (interquartile range [IQR] 13–38 years). Most contacts (64,545 [87.0%]) were successfully traced, leading to the detection of 396 secondary case-patients. The median delay between last contact with the primary case-patient and first contact by the contact tracing teams was 4 days (IQR 3–6 days).

Figure 1

Epidemic curve and symptom onset dates among Ebola virus disease case-patients, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020. A) Epidemic curve by date of symptom onset. Case-patients and contacts were divided into 2 epidemic waves, according to the date of symptom onset among case-patients (first wave, July 31, 2018–February 28, 2019; second wave, March 1, 2019–April 26, 2020). B) Distribution of dates of symptom onset among case-patients, by number of listed contacts. Data were smoothed by using a nonparametric (Gaussian) kernel-based estimate, with automatic bandwidth selection (37.6 days).Figure 1. Epidemic curve and symptom onset dates among Ebola virus disease case-patients, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020. A) Epidemic curve by date of…

Disease onset dates spanned the period from July 31, 2018, to April 26, 2020, and was bimodally distributed, showing 2 waves that peaked in October 2018 and June 2019 (Figure 1, panel A). The second wave followed a period of insecurity in this conflict-affected area that severely hampered response activities (26).

Figure 2

Flowchart showing breakdown of observed case-patients by number of listed and infected contacts among Ebola virus disease case-patients, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020.Figure 2. Flowchart showing breakdown of observed case-patients by number of listed and infected contacts among Ebola virus disease case-patients, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26,…

Figure 3

Frequency distribution of Ebola virus disease case-patients, by number of listed contacts, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020.

Figure 3. Frequency distribution of Ebola virus disease case-patients, by number of listed contacts, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020.

The median number of contacts among all case-patients was 61 (IQR 18–120), but this number was significantly lower during the first wave than the second (34 vs. 80; p<0.001). Case-patients infected in the first wave were more likely to have 0 listed contacts than those in the second wave (31.3% vs. 9.6%; p<0.001 by χ2 test), and second-wave case-patients were more likely to have a large number (>100) of contacts (Figure 1, panel B). A total of 792 case-patients (85.8%) reported >1 contact (Figure 2, 3), among whom the median number of contacts was 74 (IQR 36–134) and the mean number of contacts was 102.

A total of 64,545 contacts (87.0%) were successfully traced, of whom 308 were confirmed as EVD case-patients and 88 died during follow-up. Therefore, the inferred total number of infected contacts was 396 (308 + 88), or 0.7% of the contacts successfully traced to completion of the follow-up period. Precise detail on the mechanism of identification of confirmed case-patients among contacts is not available; although we assume these mechanisms were identified by contact tracers during follow-up, the role of other surveillance activities cannot be excluded.

Figure 4

Frequency distribution of Ebola virus disease case-patients with infected contacts, by number of infected contacts, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020.Figure 4. Frequency distribution of Ebola virus disease case-patients with infected contacts, by number of infected contacts, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020.

We observed substantial overdispersion in the offspring distribution of secondary case-patients; 80% of onward transmission was linked to only 13.9% (95% CI 11.4%–16.2%) of primary case-patients, and all secondary case-patients concentrated among the contacts of 207 (22.4%) case-patients. Further, only 99 (10.7%) primary case-patients led to >1 secondary case-patient (Figure 2, 4). We estimated k as 0.27 (95% CI 0.20–0.33).

Male contacts had slightly (but statistically significantly) greater odds of being lost to follow-up (odds ratio [OR] 1.06, 95% CI 1.01–1.11) (Table 1). Contacts in older age groups had significantly greater odds of being lost to follow-up compared with contacts in the youngest age group (0–15 years). We observed the greatest effect among contacts >60 years of age (OR 1.65, 95% CI 1.47–1.86) and a marginally smaller effect among contacts 45–59 years of age (OR 1.55, 95% CI 1.43–1.69). Conversely, contacts traced during the second wave had lower odds of being lost to follow-up (OR 0.83, 95% CI 0.79–0.88).

CRC Modeling
Completeness of Contract Tracing for Case-Patients with >1 Listed Contact

Figure 5

1 contact listed, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020..”>Figure 5. Observed (gray) and fitted (geometric; blue) zero-truncated distribution of the total number of contacts for case-patients with >1 contact listed, Beni Health Zone, Democratic Republic of the Congo,…

Among case-patients with >1 contact listed, the best-fitting distribution of the count of case-patients with any contacts was given by the zero-truncated geometric model, which produced the lowest AIC and BIC (Appendix Table 1). This distribution was very long-tailed (Figure 5), indicating that most case-patients with contacts were successfully detected, given that with an increasing mean of any count distribution, the probability for a zero count becomes smaller. This pattern is observed from the expression of the geometric distribution, described previously, where for x = 0 (i.e., the zero count), its estimated probability p0 resolves the equation

to return

, where µ is the mean of the geometric model; the larger the mean, the smaller the probability of x = 0.

We estimated (the unobserved number of case-patients with any contacts) = 8 (95% CI = 8–10), where sample size (n) was 792 and p0was found as 0.01. The sensitivity of contact tracing to detect case-patients with any contacts was therefore 792/(792 + 8) = 0.99% (95% CI 0.99%–0.99%). We observed no difference in sensitivity by epidemic wave (wave 1 = 0.99% [95% CI 0.99%–0.99%]; wave 2 = 0.99 [95% CI 0.99–0.99]).

Completeness of Contact Tracing for Case-Patients with Infected Contacts

Figure 6

1 infected contact listed, Beni Health Zone, Democratic Republic of the Congo, July 31, 2018–April 26, 2020.”>Figure 6. Observed (gray) and fitted (geometric; blue) zero-truncated distribution of the total number of infected contacts for case-patients with >1 infected contact listed, Beni Health Zone, Democratic Republic of…

Among case-patients with infected contacts, the best-fitting distribution of the count of case-patients with infected contacts was again given by the zero-truncated geometric model, which produced the lowest AIC and BIC (Appendix Table 1). This distribution is concentrated on the lower counts from 1 to 4 (Figure 6), indicating that a substantial proportion of case-patients with infected contacts may not have been detected.

We estimated (the unobserved number of case-patients with infected contacts) = 227 (95% CI 171–241), where sample size (n) was 207 and p0was found as 0.52. The sensitivity of contact tracing to detect case-patients with infected contacts was therefore 207/(207 + 227) = 0.49% (95% CI 0.43%–0.55%). We observed a statistically significant difference in sensitivity by epidemic wave, with lower sensitivity during wave 1 (0.24% [95% CI = 0.11%–0.38%]) than during wave 2 (0.48% [95% CI = 0.40%–0.56%]). Among the 792 case-patients with >1 listed contact, those with 0 infected contacts had fewer contacts overall, were slightly older, and were slightly more likely to be women or girls compared with the other groups (Table 2).

Discussion

Our findings suggest that contract tracing efforts were very successful at identifying case-patients with >1 contact but much less successful at identifying case-patients with contacts who later had EVD symptoms. This finding is unsurprising, given that the investigation component (typically by interview with case-patients under treatment, their caregivers, or both) is easier to conduct than the tracing component (typically requiring daily visits to a large number of difficult-to-locate and mobile persons). This difference has important implications, because infected contacts contribute to ongoing chains of transmission when case investigation and contract tracing is inadequate; to prioritize scarce resources, control efforts should target those case-patients among whose contacts secondary infections arise (20,21,27). A high proportion of case-patients listed >1 contact (≈85%), compared with 27% during an EVD outbreak in Liberia (28) and 44% during an EVD outbreak in Sierra Leone (27), suggesting that lessons about enhancing the quality of contract tracing were learned from previous EVD outbreaks (4,5,27,28).

Case-patients with infected contacts had more contacts on average, which may result from 3 possible explanations. First, case-patients with more contacts are more likely to have >1 infected contact among these. Second, fewer overall listed contacts may be the result of poorly conducted case investigations. We found some evidence in support of this; the mean number of contacts increased as the epidemic progressed, indicating possible improvements in case investigation quality over time as staff became more accustomed to the procedure and community trust and engagement in the response improved (29). Third, case-patients with infected contacts may differ from other case-patients; in this study, such case-patients were younger and more likely to be men or boys, which are demographic factors previously shown to affect transmission of EVD and other diseases (3032). Case-patients with more contacts have been shown to play a greater role in disease transmission and are more likely to have infected contacts (33,34). This tendency is particularly true of diseases that demonstrate heterogeneous transmission, including EVD and COVID-19, and our results suggest a high degree of overdispersion and superspreading, consistent with what has previously been reported during large EVD outbreaks (23). Overdispersion can lead to rapid expansion, particularly among hidden chains of transmission, and a promising area of research is to identify correlates of superspreading to better target limited resources for greatest impact. Previous research suggests that if highly infectious persons can be predictively identified and targeted, the efficiency of control can be greatly enhanced, such that focusing half of all control effort on the most infectious 20% of case-patients can improve effectiveness up to 3-fold (20,21).

Although estimating the number of unobserved case-patients with (infected) contacts is possible, identifying whether these case-patients have been misclassified as having 0 (infected) contacts or if they were undetected by the surveillance system in general is not possible. However, the greater probability of having 0 contacts listed during the first epidemic wave suggests substantial misclassification and suboptimal performance in the period during which surveillance activities were being established, as reported during previous EVD outbreaks (4,27,28). The sensitivity of contact tracing to detect case-patients with infected contacts was lower, and loss to follow-up greater, during the first epidemic wave, indicating quality improvements of this activity over time, either because the ability to conduct contact follow-up was hampered by the insecurity experienced during the first wave or because of greater familiarity with, and acceptance of, the process among contact tracing staff and the local population during later efforts.

Although the method we describe proposes a robust framework to assess the sensitivity of contact tracing, limitations include that no standard list of contacts against which to validate this method exists. However, the method itself has been validated to estimate actual population size in various other settings (25). The dataset does not permit the distinction between case-patients who were confirmed to have no contacts after a thorough case investigation and case-patients having no listed contacts because of no (or inadequate) case investigation. However, our method may help to identify the magnitude of the misclassification arising from this limitation. The inferences made are exclusively informed by the definition of case-patients as defined by contact tracing protocols; for example, our results would not inform the sensitivity of contact tracing as applied to asymptomatic EVD case-patients if these persons are not part of the testing strategy.

Differences in performance between contact tracers could result in strong heterogeneity in the count distribution, which might be detectable. For this reason, we applied Chao’s estimator (which allows for heterogeneity), and only if this was significantly different from the model-based estimate would we consider that an issue exists. In our results, we did not observe such a difference (Appendix Table 2, 3). Finally, we have not adjusted for observed heterogeneity, such as age, sex, profession, geographic location of the case-patients, and delays in the contact tracing process. Further work is planned to incorporate such considerations.

In conclusion, contact tracing is crucial to the containment of certain disease outbreaks. However, as with many surveillance activities, contact tracing has the potential to suffer reduced effectiveness from underreporting and poor sensitivity (4,27,28). The consequences of poor ascertainment and misclassification can be disastrous, potentially creating explosive expansion among hidden chains of transmission, particularly during containment and de-escalation phases.

We have described a novel application of CRC models to estimate a crucial yet elusive performance indicator of a key component of the public health response to epidemics, namely the sensitivity of contact tracing, as applied to a recent outbreak of EVD. The method demonstrated that most case-patients with any contacts were observed, suggesting that the case investigation component of contact tracing performed well, whereas less than half of case-patients with infected contacts were observed, suggesting that the contact follow-up component of contact tracing performed poorly in this setting. The approach described is disease-agnostic and can be extended to assess the sensitivity of contact tracing for any disease, including COVID-19, for which contact tracing has been identified as a crucial component of response activities.

Mr. Polonsky is an epidemiologist with the World Health Organization, Geneva, Switzerland. His research interests include various aspects of public health response in humanitarian and emergency settings, with a primary focus on infectious disease outbreaks and monitoring the health impact of crises.

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Shaman King (2021) Episode 34 Release Date and Time, Spoilers, Preview, Countdown, Watch Online

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If you are looking for entertaining manga then here is a fabulous detail available for you. Here we going to tell you all the necessary detail that you should know about it. According to the available details, this time Shaman King (2021) Episode, 34 is going to give you an amazing level of entertainment. You will know various things such as cast, spoiler, release date and many more other things. So let’s begin to know the complete information which is important to know.

Shaman King (2021) Episode 34 Release Date

Talking about the popularity of this amazing manga then we want to tell you various things such as 6.66/10 ratings. Which is a clear sign that you will enjoy the story and the latest episode is also ready to bring a massive level of suspense, thrill and fun for you. There are many things which you should know.

Shaman King (2021) Episode 34 Release Date

The anime is going to get huge attention in the upcoming days. While on the other side, talking about the release date then we want to tell you that you will see this episode on December 2, 2021. The episode is titled ‘To Be King’.

Now if we discuss the streaming platform then we will tell you that it will available on Netflix Japan. Most possibly you can watch the stream of this platform on your screen. If you Netflix subscription then it will be a good thing but if you don’t have the subscription then you have to take a subscription to this platform where you want to watch the stream of this latest episode 34.

Along with it, details say that its next micro-season of Shaman King (2021) will become available on the streaming platform on December 9, 2021, just 2 weeks from now.

Shaman King (2021) Episode 34 Spoilers Preview

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Because the timing of the release is the most important thing and we want to tell you that the above-mentioned timing is the official timing of the release. You can see the timing and fix the schedule in your clock.

Definitely, timing as per your region is available above. If any of the detail still unavailable here then you can ask us via the comment section. We will definitely solve the issue. Even you can bookmark the page in your browser and get the details on time.



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Devan Nicole Elayda Dies In Accident: TikTok Star Fresno State Student Devan Nicole Elayda Death Cause

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The quite heart-wrenching incident is again reported from Cedar Avenue, which made plenty of people shocked, because a famous TikToker has lost her life in a hit-and-run incident at the same spot. Yes, you heard right, the accident occurred on Saturday, 27th October 2021, and the victim has been identified as Devan Nicole Elayda a 23-years-old social media influencer and rising TikTok star. Ever since the news took place on social media a wave of great sorrow surrounded everyone especially those who are close to her, as nothing is more heartbreaking than losing our close one, get to know more check the details given below.

Devan Nicole Elayda Dies In Accident: TikTok Star Fresno State Student Devan Nicole Elayda Death Cause

As per the exclusive reports or sources, the woman was driving approximately 180 when she thought to change her seat with the fellow near California Highway Petrol. As soon as she took step towards changing a silver 2017-2020 Lexus IS smashed into Nicole, and on the spot, she lost her precious life. This is the reason everyone says that ” carelessness can lead to a massive accident”, those who saw the incident through their eyes in short witnesses, their goosebumps have appeared highly, because the accident sound was too frightening that no one could even be supposed.

Who Was Devan Nicole Elayda?

23-years-old Devan Nicole Elayda was a college student, content creator, and social media influencer based in Fresno State. More than 50k people have followed her on TikTol this is the reason everyone loved to follow her. She had seen active on various social media platforms quite often because she loved to post her photos and videos on the app and therefore, around 5k people have followed her on Instagram. But she is no longer among us which is a matter of great sorrow and therefore, everyone is paying tribute to her.

It is being reported, that the driver did not offer any first-aid to the victim even he fled instantly which was inappropriate enough, because at the time of the accident she left some breath that can become the cause of her aliveness. But due to the carelessness of the driver she had to leave the world, because of the highway, no residential was near. But the investigation to find the culprit, but hitherto, no further details have been issued by the concerned department. But as soon as the investigation is going ahead several unknown facts are revealed.

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Emma Sweet, Missing 2-Year-Old, Found Dead In White River Who Killed Emma Sweet?

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Columbus authorities have ended the investigation of finding a 2 years old Emma Sweet who was found dead in White River. On behalf of the reports, duck hunters recovered her truck sunk in the east fork of the White River in Bartholomew County. She was found dead inside the truck. As per the reports, she along with her father Jeremy Sweet who was 39 years old reported missing a week ago. As the reports disclosed that issued Jeremy was found inside the truck but Emma wasn’t there. The search for Emma is continued by the authorities so far. Get more information regarding Emma Sweet Dead or Alive.

Emma Sweet, Missing 2-Year-Old, Found Dead In White River Who Killed Emma Sweet?

The missing report of Emma and Jeremy had been filed by Emma’s mother a week ago on Thanksgiving Day.” confirmed by County Sheriff Matt Myers. He further informed that Jeremy was rushed to the hospital for the examination. Along with that, an intensive search has been begun for Emma was launched by numerous agencies. Bartholomew County Sheriff Matt Myers explained all the scenarios when he investigated along with The Indiana Department of Natural Resources and Indiana State Police. The official said that the 2 years old girl was found dead in the White River while the father survived who is admitted to the hospital in a critical condition.

What Happened To Emma Sweet?

He informed that the body of the girl was recovered at 11 AM on Sunday by ISP divers. One of the images of Emma Sweet provided as well shows her sitting in her car in the department after alerting the authority regarding the case on Friday. The authorities found the truck along with Emma Sweet’s dead body and Jeremey Sweet. The official also informed that the girl’s body was recovered about 21/2 miles away from where her father found it in the truck.

Sweet and his truck were found at around 6 in the morning and search continued for Emma found at around 11 in the morning as we informed you above. Firefighters were searching for Emma who was later found in the water. Divers pull out her body from the water, it is being said that body of the girl was caught by a debris field in the river. The family members of Emma are appreciating the authority for the rapid investigation however, the family faced a huge loss. We will get back to you with the rest of the information regarding the case. Stay tuned with Social Telecast for the latest worldwide information.

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