Myths COVID
Myths COVID
Myths COVID
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RESEARCH ARTICLE
Abstract
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Funding: The author(s) received no specific and hand sanitizers (79.1%). Perceived inhibitors were categorized into three factors: two
funding for this work. misperceived, myths (31.6%) and false assurances (32.9%), and one correctly identified;
Competing interests: The authors have declared engagement in standard precautions (17.1%). Myths about protection from the virus involve
that no competing interests exist. perceived religiosity and effectiveness of selected food items, hot weather, traditional medi-
cine, and alcohol drinking, ranging from 15.1% to 54.7%. False assurances include people’s
perception that they were living far away from areas where COVID-19 was rampant
(36.9%), and no locally reported cases were present (29.5%). There were tremendous infor-
mation needs reported about COVID-19 concerning protection methods (62.6%), illness
behavior and treatment (59.5%), and quality information, including responses to key unan-
swered questions such as the origin of the virus (2.4%). Health workers were perceived as
the most at-risk group (83.3%). The children, adolescents, youths were marked at low to
moderate (45.1%-62.2%) risk of COVID-19. Regional, township, and access to communica-
tion showed significant variations in myths, false assurances, and information needs (p
<0.05).
Conclusions
Considering young population as being at low risk of COVID-19 would be challenging to the
control efforts, and needs special attention. Risk communication and community engage-
ment efforts should consider regional and township variations of myths and false assur-
ances. It should also need to satisfy information needs, design local initiatives that enhance
community ownership of the control of the virus, and thereby support engagement in stan-
dard precautionary measures. All forms of media should be properly used and regulated to
disseminate credible information while filtering out myths and falsehoods.
Introduction
The novel-coronavirus disease 2019 abbreviated as COVID-19 is currently a pandemic as
declared by the World Health Organization (WHO) on January 30, 2020 [1]. The outbreak
was first reported in late December 2019, when clusters of pneumonia cases of unknown etiol-
ogy were found to be associated with epidemiologically linked exposure to the seafood market
and untraced exposures in the city of Wuhan of China [2, 3].
The disease is highly infectious, and its main clinical symptoms include fever, dry cough,
fatigue, myalgia, and dyspnea. Globally, 1 in 6 of the patients with COVID-19 develops to the
severe stage, which is characterized by acute respiratory distress syndrome, septic shock, diffi-
cult-to-tackle metabolic acidosis, and bleeding and coagulation dysfunction [4, 5]. Epidemio-
logically, the distribution of the disease is exponentially growing across the globe. For example,
on this date, June 9, 2020, the pandemics registered 7, 216,252 cases, and 409, 092 deaths in the
world. Of the 3, 961,425 closed cases (10%) ended up in deaths. Ethiopia has become one of
the COVID-19 affected countries as of March 12, the date on which one imported case was
first detected. Since then, the infection by the virus has kept mounting. For example, on June
5, 2020, there were 2,152 total notified cases and 27 deaths in Ethiopia [1, 6, 7].
According to the WHO reports, the COVID-19 has no effective cure, yet early recognition
of symptoms and timely seeking of supportive care and preventive practices enhance recovery
from the illness and combat the spread of the virus. Older men with medical comorbidities are
more likely to get infected, with worse outcomes [8–10]. Available evidence has shown that the
virus spreads from human-to-human mainly through respiratory droplets and body contacts.
Contact with contaminated surfaces, hands, and touching of faces-eye-nose-mouth are pre-
dominant ways to get exposed to the infected droplets [11–14].
The battle against COVID-19 continues in Ethiopia. To guarantee the final success of stop-
ping the virus, understanding myths and perceptions are so vital. Some questions require
answers. For example, who do people think that they are most at risk? What are community
perspectives about factors that facilitate the spread of the virus? What about perceived inhibi-
tors? How scientifically accepted are these perceptions? Are the perceived facilitators or inhibi-
tors correct or misperceived? Do people own the responsibility to fight the virus or externalize
it? The answers to the above questions are of paramount importance to curb the pandemic by
enhancing the probability of people’s practicing the necessary precautions. Standard precau-
tionary measures include avoidance of contact with surfaces, keeping physical distance, hand
hygiene, respiratory hygiene, using sanitizers, and protective pieces of equipment [12–14].
The WHO recommends the risk communication and community engagement efforts to
investigate and control “infodemics”, myths, beliefs, and stigma so that the spread of the coro-
navirus would be effectively combated [10, 15, 16]. For example, the WHO reported risk per-
ception, drinking alcohol, hot weather, and antibiotics related myths on COVID-19.
Moreover, up-to-date information regarding causes, means of protection, modes of transmis-
sions, diagnostic symptoms, and treatment/isolation procedures are relevant to withstand
myths, beliefs, perceptions and support preventive efforts [12, 15, 17, 18].
The public health importance of COVID-19 has been recognized by the government of
Ethiopia. There are movements to decentralize screening opportunities, quarantine, and treat-
ment centers, and promoting precautionary measures. At the moment of the study, the gov-
ernment declared a state emergency in support of the precautionary measures, and has taken
public measures such as the closure of schools, including universities; worked with public ser-
vice outlets to install locally available preventive technologies, including handwashing
machines; limiting the number of passengers in public transport, among others. Moreover, the
ministry of health engaged in public awareness creation, risk communication, and community
engagement tasks, and rallying voluntary activities. Now, addressing community beliefs, per-
ceptions, and information gaps would reinforce the efforts to stop the virus. Therefore, this
study aimed to assess community myths, beliefs, perceptions, and information needs via an
online nationwide survey in Ethiopia.
Data analysis
Participants’ online responses were first encoded on an Excel database and later exported to
SPSS version 20.0 for analysis. Respondents’ background variables and specific belief items are
presented in the frequency tables. Standardized mean scores (0–100) and standard deviations
were used to describe lists of categories of factors according to themes of perceptions they
belonged to. One-way analysis of variance (ANOVA) and t-test were computed to compare
the mean differences by region, township, and access to communication. A multi-response
analysis was performed for every perception. A 95% confidence interval and a p-value of less
than 0.05 were used to claim statistically significant association.
Results
Socio-demographic characteristics of participants
A total of 929 participants from all regions of Ethiopia responded to this online survey ques-
tionnaire. Table 1 presents background information of the survey respondents. A majority of
the respondents were in the age range of 30–39 years (50.8%), from Zonal towns (56.0%), and
the Oromia region (56.6%).
https://doi.org/10.1371/journal.pone.0243024.t001
The items that contributed to behavioral non-adherence include that people still shake each
other’s hands, do not seek care for symptoms suggestive of COVID-19, use crowded transport
means, are not being screened for flu-like symptoms, and fear of stigma with respective
decreasing order of factor loading scores (0.714–0.503). The second category of perceived facil-
itating factors was the lack of enabling environmental conditions that are supposed to support
adaptations of precautionary measures. The lack of enablers was made up of economic reasons
that challenge stay at home principle, overcrowded living/working conditions, absence of PPE
like face masks, and sanitizers with decreasing order of factor loading scores (0.786–0.718).
The behavioral non-adherence and lack of enablers related factors explained an overall vari-
ance of perceived facilitators of the virus by 48.8%.
Table 2. Perceived categories and lists of exacerbating factors of COVID-19, May 2020, Ethiopia.
Perceived COVID-19 exacerbating factors Principal components and factor loading score Descriptive statistics
Behavioral non- adherence Lack of enabling environment Freq. % (95% CI)
People fear stigma and bias related to COVID-19 .503 584 62.9 (59.7,65.9)
People still use crowded transportation means .654 562 60.5 (57.4,63.3)
People with flu-like symptoms are not well screened for COVID-19 .638 551 59.3 (56.1, 62.5)
People do not often seek care for symptoms that looks like COVID-19 .681 481 51.8 (48.7, 55.1)
People still hug and shake each other’s hands while greeting .714 416 44.8 (41.5. 47.8)
People do not stay at home for economic and social reasons .786 858 92.4 (90.6,94.2)
People still live and work in a very crowded condition .705 814 87.6 (85.4, 89.6)
People do not have PPE like face masks .727 758 81.6 (78.9, 84.0)
People do not have hand rub alcohol or sanitizers .718 735 79.1 (76.3,81.6)
Notes: KMO = 81.9%); Variance Explained (VE = 48.8%); and PPE: Personal Protective Equipment.
https://doi.org/10.1371/journal.pone.0243024.t002
Perceived inhibiting factors: How do people think about the slow down of
COVID-19?
Classifications of inhibitors. Table 3 presents categories and lists of perceived inhibitors
of COVID-19 spread with their respective prevalence. EFA produced three principal categories
of perceived factors inhibiting COVID-19. Two of the three categories were misperceived
(myths and false assurances), while one was correctly perceived inhibitor. The myths category
was composed of factors that are believed to inhibit the virus without having been scientifically
proven. In this case, the myths include: eating selected foods (garlic, onion, ginger, etc) for pre-
vention and cure; perceived religiosity (perceiving oneself as an effective religious man/
woman in controlling challenges); drinking alcohol; people’s perceived confidence that they
owned effective traditional medicines that were, however, not clinically confirmed; and living
in hot weather. The factor loading scores in respective order ranged between 0.764–0.488. The
second category of perceived inhibitors was still local sayings that were often related to false
Table 3. Perceived categories and lists of inhibiting factors of COVID-19, May 2020, Ethiopia.
Perceived COVID-19 inhibiting factors Principal components and factor loading scores Descriptive
statistics
Myths Invulnerability (false assurances) Engaged in precautions Freq. % (95% CI)
We are religious enough to control COVID-19 .496 508 54.7 (51.5,
58.0)
We are eating garlic, onion, honey among others to prevent COVID- .764 455 49.0 (45.7,
19 54.3)
The weather we live-in is too hot for coronavirus to survive .488 242 26.0 (23.6,
29.1)
We are eating garlic, onion, honey among others to cure COVI-19 .728 227 24.4 (21.6,
27.2)
We believe we have traditional medicine against COVID-19 .511 165 17.8 (15.5,
20.3)
We are drinking alcohol to protect against COVID-19 .676 140 15.1 (12.9,
17.3)
There are no locally reported COVID-19 cases so far .770 343 36.9 (33.8,
39.7)
We live far away from COVID-19’s rampant areas .661 274 29.5 (26.8,
32.4)
Engaged in standard precautions measures of COVID-19 .775 159 17.1 (14.9,
19.7)
https://doi.org/10.1371/journal.pone.0243024.t003
assurances that people were protected from COVID-19 (unlike myths, the second category of
beliefs may not need scientific approval or disapproval). The category consisted of two main
beliefs: “we live far away from COVID-19 rampant areas” and “there are no locally reported
COVID-19 cases so far”, with factor loading scores (0.770–0.661). The beliefs looked false
assurances in that people perceive themselves as living out of a risk zone that is an impression
of invulnerability. The third, correct, and promotable category of perceived inhibitors was a
single item about people having been engaged in standard precautions (factor score load-
ing = 0.775). Factors related to the above three categories explained an overall variance of per-
ceived inhibitors by 54.6%, indicating the presence of several other unreported myths and
unhealthy beliefs that need further assessment.
Prevalence of inhibitors. Descriptive statistics columns in Table 3 indicate the prevalence
of perceived inhibitors. Myths and false assurances were the most prevalent perceived inhibi-
tors of the spread of COVID-19 compared to the perception that engagement in precautionary
measures protect from exposure to and spread of the virus. Specifically, perceived religiosity,
effectiveness of selected foods, and perceived protectiveness of hot weather were the common-
est myths, accounting for 508 (54.7%), 455 (49.0%), and 242 (26.0%), respectively. Beliefs that
there were no locally reported cases of COVID-19, and the specific localities where respon-
dents are currently living are far away from coronavirus rampant areas contributed to 343
(36.9%) and 274 (29.5%) respective prevalence of false assurances. On the other hand, the
prevalence of a perception that the spread of COVID-19 would be controlled as a result of peo-
ple’s active engagement in standard precautionary measures was as low as 159 (17.1%). Over-
all, false beliefs and myths were more rampant than accurate perceptions about factors that
potentially inhibit the spread of COVID-19. About153 (16.5%, 95%CI:14.2–18.8%) respon-
dents reported that they were unsure of other factors which potentially inhibit the distribution
of COVID-19 given the virus is newly introduced
Table 4. Perceived categories and lists of information needs about COVID-19, May 2020, Ethiopia.
Perceived information need factors about COVID-19 Principal components and factor loadings scores Descriptive
statistics
Preventive Illness and Quality Diverse Freq. % (95% CI)
treatment information questions
How to protect from COVID-19 .816 605 65.2 (62.2,
68.2)
Exhaustive transmission modes .839 554 59.6 (56.3,
62.9)
Distinguishable symptoms .842 529 56.9 (53.9,
60.3)
Details on isolation and quarantine .683 611 65.8 (62.8,
68.9)
What to do when they or someone become symptomatic (illness .534 581 62.5 (59.3,
behavior) 65.7)
Nature and process of treatment .786 552 59.4 (56.4,
62.4)
What to do with risk factors or as a risk group .587 412 44.3 (41.1,
47.6)
Change provoking information�� .643 27 2.9 (1.8, 4.1)
True and update information .867 12 1.3 (0.5, 2.0)
Diverse information needs� .907 14 1.5 (0.6, 2.2)
Notes: Kaiser Mayer Olkin’s measure of sampling adequacy (KMO = 80.5%), Variance explained (VE = 65.4%).
�
Diverse information need: learn about capacity and readiness of the health facilities to manage in transmission peaks, costs related to treatment services, community
screening service, want to differentiate the origin of the disease itself as to whether it is a Wrath of the Creator or biological weapon, need praying, among others.
��
Change provoking information: bridging knowledge to behavior change, Alleviation of reluctance to precautions, messages involving a specific audience, increasing
vulnerability perception, repeatedly accessing with messages, enforcement of laws that save guard lives, implementations of command posts in favor of combating
COVID-19, how the jobless can be economically supported, where to get sanitizers, among others.
https://doi.org/10.1371/journal.pone.0243024.t004
Table 5. Perceived COVID-19 risk groups and labels, May 2020, Ethiopia.
Perceived high-risk groups Descriptive statistics
Freq. % (95% CI)
Health workers 773 83.2 (80.7, 85.7)
People with underlying illness conditions 732 78.8 (76.1, 81.4)
Elderly people 709 76.3 (73.6, 78.9)
Adults (30–50 years old) 597 64.3 (60.9, 67.3)
Youth (16–29 years old) 578 62.2(59.1, 65.2)
Pregnant women 552 59.4 (56.5. 62.5)
Adolescents (10–15 years old) 448 48.2 (45.0,51.3)
Children (0–9 years old) 419 45.1 (41.9,48.3)
https://doi.org/10.1371/journal.pone.0243024.t005
Table 6. Descriptive statistics and regional ranges for perceptions and needs, May 2020, Ethiopia.
Beliefs and information need categories Median %mean(±SD) Regional ranges p-value
Perceived facilitators (overall) 66.7 69.5 (±15.6) 62.8–73.5 0.239
Behavioral non-adherence 60.0 55.9 (±11.2) 49.0–61.0 0.323
Lack of enabling conditions 85.5 86.5 (±6.5) 80.1–89.2 0.262
��
Perceived inhibitors (overall) - - - -
Misperceived inhibitor: Myths 33.3 31.6 (±11.2) 24.8–36.9 0.002�
Misperceived inhibitor: False assurance 36.3 32.9 (±4.6) 25.5–49.5 <0.001�
Engagement in standard precautions 17.0 17.1 (±2.5) 6.7–22.5 0.146
Information need (overall)��� 58.3 59.3 (±3.4) 52.4–65.3 0.031�
Prevention related 66.7 62.6 (±8.1) 50.6–66.7 0.021�
Illness and treatment-related 53.2 59.5 (±8.9) 53.1–63.6 0.317
Quality information 3.6 2.4 (±1.4) 0.0–2.4 0.590
Mixed information need 1.5 1.7 (±0.8) 1.1–4.1 0.443
�
Statistically significant at p <0.05 (two-tailed)
��
Overall perceived inhibitor has two misperceived (myths and false assurances) and one correctly perceived (engaged in standard precautions) aspect, needing no
further merging for an overall score.
���
The overall mean of information needs to exclude the two dimensions-quality and mixed needs because of extreme values.
https://doi.org/10.1371/journal.pone.0243024.t006
Fig 1. Diagram of regional distribution of perceptions about COVID-19, May 2020, Ethiopia.
https://doi.org/10.1371/journal.pone.0243024.g001
Fig 2. Diagram of township distribution of perceptions about COVID-19, May 2020, Ethiopia.
https://doi.org/10.1371/journal.pone.0243024.g002
Fig 3. Diagram of distribution of perceptions by access to communication platforms, May 2020, Ethiopia.
https://doi.org/10.1371/journal.pone.0243024.g003
Discussion
This online survey has generated pertinent findings of nationwide community perceptions
concerning factors that facilitate and inhibit a spread of COVID-19, risk labeling, and informa-
tion needs in Ethiopia. The perceived factors were aligned into the following main categories:
behavioral adherence, lack of enabling environmental conditions, myths, false assurances,
engagement in standard precautions, and information needs about prevention, illness behav-
ior and treatment, including answers to diverse questions related to the origin, a spread and
control of the coronavirus. Each perceived factor was discussed step by step as follows:
This study found a moderate perception of severity by the community, 70.8%, while, some-
what low perceived vulnerability, 57.8%. This indicates the community’s perception of risk
should be increased further. The perceptions were measured by a single item for each. There
were two forms of risk labeling and groups in the community: As perceived by the community,
young people below 30 were perceived as a low-moderate risk with an increasing order: 0–9
years old (45.1%), 10–15 years old (48.2%), 16–29 years old (62.2%), and 30–50 years old
(64.3%). Health workers, people with underlying illnesses, and the elderly were perceived as
high-risk groups with the respective prevalence of 83.2%, 78.8%, and 76.3%. Perhaps, the high-
risk groups perceived by the community, in this study, were consistent with that of the WHO.
According to WHO, frontline health workers, people with underlying illness, and elderly peo-
ple are high-risk groups [22, 23]. The correct perception of the high-risk group is important
for giving protection priorities against infection by COVID-19. However, this study reported
that children, adolescents and youths were relatively perceived as lower risk groups (45.1%,
48.2%, and 62.2%, respectively). This would be concerning to the control efforts to some
extent. We argue that those who were perceived as being at low-risk would act as reservoirs for
a spread of COVID-19 for a couple of reasons: one, about 63% of the Ethiopian population
aged < 25, with a median age of 19.5, and these segments pass time searching for jobs like
daily labors [20, 21]. Two, in one of the previous studies conducted in Ethiopia, 179 (72.5%) of
respondents knew that the elderly and people with underlying illnesses are high-risk groups,
while only 15 (6.1%) knew that young adult people must engage in precautions just like any
other segment [25] Therefore, some enforcement needs to control a potential contribution of
youths in the transmission loop as the current perception of risk groups stands.
Factors that were perceived to exacerbate the spread of the virus were teamed up into two
thematic categories: behavioral non-adherence (55.9%), and lack of enabling environmental
conditions (86.5%). Behavioral non-adherence, in this case, referred to individuals and social
ignorance, disregard, and lack of commitment to convert standard precautionary measures
that seem to be under the control without needing much material support. The ignorance and
lack of commitment were illustrated by the following community’s experiences: people still
hug each other and shake hands while greeting, do not often seek care while showing symp-
toms that look like COVID-19, still feel comfortable to use crowded unventilated transport
means, and fear stigma-related to the virus. Interestingly, the use of crowded/unventilated
transport means was not only due to lack, but rather it also was involved in behavioral non-
adherence. Theoretically, people often rationalize their engagement in preventive actions, and
rationalities should be carefully studied and justified [26]. On the other hand, lack of enabling
environments is about condition and resource factors whose presence or absence enable peo-
ple to take precautionary actions. Some of them can be illustrated as such people cannot stay at
home for economic and social reasons, do not have personal protective equipment (PPE) like
face masks, do not have hand-rub alcohol or sanitizers, and still live and work in crowded con-
dition. In this study, the magnitudes of both behavioral non-adherence and perceived lack of
environmental conditions were high, irrespective of regions and townships. Behavioral and
communication theories indicate that people’s perceived lack of resources negatively affects
actual practices [26]. Nonetheless, the high prevalence of perceived facilitators signals two
main urgencies. One, it suggests strong work to alleviate behavioral non-adherence, and lack
of enablers that facilitate the spread of the virus. Two, even a higher perceived lack of enabling
conditions looks concerning given that it may lead people to externalize the capacity to control
the virus, while ignoring to their personal efforts. Thus, to convert this perception into oppor-
tunity, local initiatives that support engagement in standard precautions should address the
locally perceived barriers, and enhance a shared responsibility and community ownership to
involve in efforts of combating COVID-19 [10].
Factors that were perceived as inhibitors of the spread of the virus were classified into three:
false assurances (32.9%), myths (31.6%), and engagement in standard precautions (17.1%).
Interestingly, the first two of the three factors were wrongly perceived inhibitors, that was why
we labeled them myths and false assurances. False assurances were impressions of invulnerab-
lities, and characterized by people’s perception that they were living out of the COVID-19 risk
zone. In the current study, the two main false assurances were the perceived absence of locally
reported COVID-19 cases and residence far away from COVID-19 rampant areas. One study
from the Kingdom of Saudi Arabia presented walking through sanitized gates could give a
false sense of protection and potentially deceit the passersby from taking the recommended
preventive actions [27]. In the current study, myths include: perceived effectiveness of religios-
ity (54.6%), food items (49.0%), living in hot weather (26.0%), traditional medicines (17.8%),
and drinking alcohol (15.1%) to protect from COVID-19. WHO myth busters list out most of
the misperceptions presented in this study, indicating that these were globally shared alto-
gether with the pandemic [15]. Pieces of evidence indicate that myths or misperceptions like
denial of the presence, and misperceived causes, transmissions modes, and protection ways
can set back preventive and control efforts in times of the pandemics of HIV, Zika virus, Yel-
low fever, and Ebola, unless traced and addressed [28–31]. The magnitude of the correctly per-
ceived factor (engagement in standard precautionary measures) for inhibiting the spread of
COVID-19 was too low (17.1%), demanding hard work to promote this perception until a
larger segment of the community embraces an accurate reason for protection from the virus.
The finding from the current study revealed that the majority of the information needs
were related to protection methods that are symptoms, mode of transmission and prevention
(56.9%-65.2%), and procedures to be followed when someone feels ill from COVID-19 or at
risk of contracting it, including isolation, quarantine, and treatment (44.3–65.8%). Particu-
larly, people want to access information about isolation and quarantine–how it works (65.8%),
and what to do when someone becomes symptomatic (65.2%). One study in 2018 on health
information needs during the outbreak of Ebola showed that there was a need to an uninter-
rupted access to an up-to-date information including about causes, transmission modes, cures,
the readiness of health facilities, and even the role of government [31]. Some studies related to
illness behavior and drug repurposing from Pakistan and Saudi Arabia revealed that misinter-
pretation or misinformation (less quality or inadequate) about treatment/medicines that were
delivered by press, electronic and social media has been leading to self-medication by chloro-
quine, hydroxychloroquine, and Ivermectin as COVID-19 cure [32, 33]. Interestingly, though
minor proportion, there were people who sought quality and change provoking information
that is true, up-to-date, how it is possible to alleviate ignorances that exist in the community
regarding the adaptation of precautions of COVID-19, at the presence of basic knowledge.
Cognitive dissonance theory recommends audience-specific messages that satisfy the informa-
tion needs to close the gaps between knowledge and practices [34]. This study found out that
some questions were left unanswered about COVID-19, one of these was the need for informa-
tion about the origin of the virus. No matter the reported magnitude of such a question, pro-
viding convincing responses would enhance the uptake and support for preventive and
treatment efforts. For example, one study from Pakistan reported that some recognized politi-
cal figures claimed conspiracy (the virus was aimed to affect Muslim countries) as to the origin
of the virus and raised public hesitancy to the COVID-19 vaccine which is under development
[35].
In this study, significant regional differences were observed on myths, false assurances, and
preventive information needs. Specifically, a slightly higher magnitude of myths and lower
information need was observed in Addis Ababa. From the date of onset until 9 June 2020,
Addis Ababa constituted about 3/4th (1,625 of 2,156 cases) of an accumulation of people with
COVID-19, as referred to in most of the daily notification note on COVID-19 situational
updates [36, 37]. Addis Ababa is located at the center of Ethiopia, geographically, politically,
and economically. Thereof, it has an enormous connection with most Ethiopian regions and
towns, which would later lead to a massive spread of the virus to the rest of the regions, due to
myths. Additionally, this study found variations in the distribution of myths based on the
township, a significantly higher accumulation was observed in big towns than zonal or district
towns. Therefore, serious attention needs to be paid to further understand and clear the
myths, particularly in Addis Ababa and other big cities/towns in Ethiopia. False assurances
that are perceived to inhibit the spread of the virus were common in the Southern region com-
pared to others. Crudely speaking, the false assurances related to the perception of living out of
risk zones may seem to go with the prevalence of COVID-19 cases reported in the Southern
region. covid-19 case distributions notified by the ministry of health currently indicated, only
15 of 2,156 (0.70%) of cases and zero death were found in the southern region until June 9,
2020 [37]. However, there is no warranty that the virus has not yet been spread across the
region, given the testing centers or testing capacity have not yet reached out well in Ethiopia at
the moment of the study. The perceptions that there were no locally reported COVID-19 cases
and people were living far away from case rampant areas may remain deceitful. Concerning
information gaps, southern regions, and zonal and district towns showed higher needs, partic-
ularly for preventive information. Currently, a vaccine is one of the most common topics peo-
ple want to get informed about, but largely affected by conspiracy theories as one of the studies
from Pakistan revealed [35].
The above records about perceptions justify that the community’s readiness and responses
against a spread of the virus would not withstand the fast-growing rate of infection, suggesting
a lot of risk communication and community engagement works. There were a couple of rea-
sons to support this idea. First, the magnitude of the correctly perceived inhibitor (engagement
in precautions) of the spread of the virus was as low as 17.1%. Second, there were high per-
ceived magnitudes of behavioral non-adherence and lack of required resources regarding
efforts to combate COVID-19. Third, myths and false assurances were rampant.
Conclusions
This assessment of the community’s perceived factors facilitating and inhibiting a spread of
COVID-19, risk group labeling, and information needs provides important signals to control
the spread of the virus. There were substantial magnitudes of perceived behavioral non-adher-
ence, lack enabling resources, myths, false surety, information needs, and low perceived
Supporting information
S1 Questionnaire.
(DOCX)
Acknowledgments
We express our heartfelt thanks to all individuals who participated in the study: respondents,
individuals who have supported data collection across the regions, and professionals who assis-
ted the operations of this online survey.
Author Contributions
Conceptualization: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Yimenu Yitayih, Argaw
Ambelu.
Data curation: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Yimenu Yitayih, Argaw
Ambelu.
Formal analysis: Yohannes Kebede, Zewdie Birhanu, Argaw Ambelu.
Investigation: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Yimenu Yitayih, Abera Jote,
Argaw Ambelu.
Methodology: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Yimenu Yitayih, Argaw
Ambelu.
Project administration: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Jemal Abafita, Ashe-
nafi Belay, Abera Jote, Argaw Ambelu.
Resources: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Yimenu Yitayih, Jemal Abafita,
Ashenafi Belay, Abera Jote, Argaw Ambelu.
Software: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Jemal Abafita, Ashenafi Belay,
Abera Jote, Argaw Ambelu.
Supervision: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Jemal Abafita, Abera Jote,
Argaw Ambelu.
Validation: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Jemal Abafita, Abera Jote, Argaw
Ambelu.
Visualization: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Yimenu Yitayih, Jemal Aba-
fita, Ashenafi Belay, Abera Jote, Argaw Ambelu.
Writing – original draft: Yohannes Kebede.
Writing – review & editing: Yohannes Kebede, Zewdie Birhanu, Diriba Fufa, Yimenu Yitayih,
Jemal Abafita, Ashenafi Belay, Abera Jote, Argaw Ambelu.
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